This article demonstrates how to automatically convert PDF slide decks into editable markdown text by first converting each page to images, then applying a deep‑learning OCR pipeline (CTPN for detection and CRNN for recognition) with Python code examples, achieving high transcription accuracy.
This article reviews two recent computer‑vision approaches—DRRG and STKM—that combine CNN backbones with graph‑based relational reasoning and self‑attention to achieve state‑of‑the‑art detection of arbitrarily shaped text in images.
This article explains the challenges of scene text detection and introduces two state‑of‑the‑art models, ContourNet and CenterNet, detailing their architectural innovations, loss functions, and how they overcome issues like extreme aspect ratios and anchor‑based inefficiencies.
This article outlines a multimodal AI framework for detecting pornographic video content by combining image and audio analysis, detailing the challenges of visual and speech-based recognition, describing the DCNet and RANet model architectures, fusion strategies, and reporting experimental accuracy of 93.4% on a 3k test set.
This article reviews two end‑to‑end deep‑learning approaches—DocUNet (CVPR 2018) and DewarpNet (ICCV 2019)—for correcting warped document images, detailing their network architectures, synthetic data generation, loss functions, experimental results, and the remaining challenges in document dewarping.
SQLFlow is an open‑source platform that lets users build and run machine‑learning and deep‑learning models directly from SQL statements, lowering the barrier for business analysts to apply AI by abstracting complex pipelines into familiar database queries.
The OSGAN method introduced by Alibaba’s Mama team and Prof. Song Ming‑Li merges generator and discriminator updates into a single stage, cutting GAN training time by roughly 1.5‑1.7× while maintaining performance, and is validated on symmetric and asymmetric DCGANs with open‑source code.
The article outlines the Tencent Advertising Algorithm Competition’s video ad challenge, details the paper submission guidelines, and shares a participant’s step‑by‑step technical approach—including baseline experiments, model re‑implementation with Paddle, multimodal feature extraction, optimizer choices, and future improvement directions—providing practical AI insights for multimedia video classification.
This article presents a technical overview of a multimodal AI framework that combines image and audio analysis to identify pornographic video content, detailing model architectures, feature extraction methods, and experimental results achieving 93.4% accuracy on a 3,000‑sample test set.
This article traces the origins, unique difficulties, method classifications, and current advancements of scene text detection, highlighting how AI has enabled computers to read images and the ongoing research to improve accuracy, speed, and multilingual support.
The paper introduces MTrajRec, a Seq2Seq multi‑task learning framework that simultaneously restores low‑sampling‑rate GPS trajectories to high‑sampling‑rate and aligns them to the road network, achieving more accurate and efficient trajectory recovery for downstream applications such as navigation and travel‑time estimation.
This article provides a comprehensive overview of machine learning, explaining core AI terminology, distinguishing statistics, statistical learning, and machine learning, outlining a three‑part learning roadmap covering mathematical foundations, algorithms, and Python programming practice, and offering curated resources for building a solid knowledge system.
The paper presents DeepDualMapper, a gated‑fusion deep network that combines aerial imagery and vehicle trajectory data to automatically generate high‑precision maps, detailing its architecture, gated and refinement modules, and experimental validation on three city datasets.
The Meituan team introduces DPIN, a three‑module deep network that jointly models ads and their positions to mitigate position bias in CTR prediction, achieving up to 2.98% AUC improvement, 2.25% higher CTR and 2.15% RPM gains while keeping latency modest, and is applicable to broader ranking tasks.
By deploying large‑scale visual AI—including a ResNet‑101 classifier, ArcFace‑trained matching features, clustering‑based sub‑category refinement, and product‑level image indexing—Xianyu’s marketplace dramatically improves image quality, removes duplicates, enhances search relevance and feed diversity, and filters non‑compliant content.
The TFCC framework, developed by WeChat's backend team, delivers high‑performance, easy‑to‑use, and universal deep‑learning inference by supporting numerous ONNX and TensorFlow operations, optimizing model structures, constants, and operators, and providing a versatile runtime and math library for both CPU and GPU platforms.
This article details the design, iterative development, and performance optimizations of 58's AI‑driven intelligent Q&A chatbot, covering its overall three‑layer architecture, the QABot, TaskBot, and answer‑recommendation modules, as well as dynamic strategy adjustment, caching mechanisms, and real‑world deployment results.
Weibo’s deep learning platform faced severe latency and stability issues when accessing massive small‑file datasets via a compute‑storage‑separated architecture, so the team adopted the CNCF Fluid project with JindoRuntime, implementing a distributed cache that leverages POSIX interfaces, dramatically improving data locality, reducing HDFS load, and achieving up to 18‑fold training speedups while raising success rates from 37 % to 98 %.
TDQA is a no‑reference, deep‑learning video quality assessment algorithm designed for live‑streaming, built on a large subjectively annotated dataset and an end‑to‑end architecture with fine‑tuned backbones, achieving state‑of‑the‑art accuracy and sub‑second inference for real‑time quality monitoring and pipeline optimization.
This article presents the challenges of travel‑related product search, explains why traditional rule‑based sorting is insufficient, and describes how Alibaba Flypig’s team built a deep‑learning based personalized ranking system—including architecture, model variants, experimental results, and future optimization directions—to improve conversion rates for flight and ticket searches.
This article explains the origins, definitions, and evolving relationship between artificial intelligence and machine learning, highlighting their historical milestones, core concepts, and how modern applications like deep learning, neural networks, and recommendation systems illustrate their intertwined development.
This article reviews the latest releases of PyTorch 1.8 and TensorFlow 2.5, outlining their new functionalities, ecosystem tools such as TensorFlow.js, Lite, and TFX, as well as PyTorch Mobile and Lightning, and provides guidance on selecting the most suitable framework for different deep‑learning projects.
The paper presents a speaker‑aware module (SAM) that enables high‑quality voice conversion using only a single utterance of the target speaker, addressing the small‑data challenge in speech timbre transfer and achieving state‑of‑the‑art performance on the Aishell‑1 benchmark.
This article reviews a GAN‑based deep learning method for removing motion blur from images, covering the problem definition, related work, the multi‑scale generator and discriminator architecture, loss functions, the GoPro dataset, and experimental results that demonstrate clear visual improvements.
The paper proposes BCNet, a lightweight bilayer instance segmentation network that explicitly models occluder and occludee relationships by treating each region of interest as two overlapping layers, achieving significant performance gains on COCO, COCOA and KINS datasets under heavy occlusion.
This article details Alibaba's AliMama brand risk identification system, covering the challenges of counterfeit detection, the construction of large‑scale brand datasets, the design of classification, logo detection, and variation models, their optimization, evaluation metrics, and future directions for AI‑driven brand protection.
Kuaishou becomes the first company to deploy a deep‑learning‑based real‑time voice‑conversion system on PC clients, delivering stable, natural‑sounding transformed speech with sub‑200 ms latency, and the article analyzes industry methods, technical challenges, and the four‑module architecture that made it possible.
This article explains how neural networks work, walks through the step‑by‑step training process of a convolutional model, showcases vivid visualizations of each layer, and demonstrates how tiny adversarial perturbations can dramatically alter predictions, highlighting the importance of AI security.
The article presents Semantic Image Matting, a novel approach that incorporates 20 semantic Alpha pattern categories into the matting pipeline via semantic Trimap, region‑based classifiers, multi‑class discriminators, and learnable gradient loss, achieving state‑of‑the‑art results on multiple benchmarks.
The paper presents a deep‑learning approach that uses an ImageNet‑pretrained CNN to predict full human rating distributions for images via an Earth Mover’s Distance loss, trained on the AVA dataset, and demonstrates accurate assessment of aesthetic factors such as tone, contrast, and composition.
The JD AI Institute and ICME2021 concluded their third face keypoint localization contest, emphasizing efficient masked‑face detection to aid COVID‑19 contact tracing, attracting top universities and tech firms, expanding data scale, and tightening model efficiency constraints to push the field forward.
This paper proposes a Path‑based Deep Network (PDN) that combines similarity‑index and embedding‑based retrieval paradigms to model user‑item interactions via Trigger Net and Similarity Net, achieving significant improvements in click‑through rate, GMV, and diversity on Taobao’s homepage feed.
Tencent Music’s Multimedia R&D Center celebrated its first appearances at IJCNN and ICASSP 2021 by having two papers accepted—one presenting large‑scale singer recognition via deep metric learning and the other describing user‑driven audio embeddings for content‑based music recommendation—highlighting the team’s expanding expertise across diverse music‑recognition technologies and future research directions.
This guide examines how deep learning techniques are applied to large‑scale code analysis, covering the technical workflow, program representations such as token sequences and AST paths, the code2vec architecture, its advantages, current limitations, and potential applications like code summarization and similarity detection.
This article presents a method that replaces manual visual inspection with an automated YOLOv5‑based object detection pipeline to classify video frames as normal, colorful, or black screens, detailing data annotation, training, loss calculation, inference code, and showing a 97% accuracy improvement over ResNet.
This article chronicles 58.com’s journey from a small brainstorming room to a full‑scale AI design platform, detailing the development of smart cutout, the BASNet segmentation model, custom loss functions, template editing, and the measurable business impact of the AI designer.
This article compiles and summarizes fifteen influential survey papers on Graph Neural Networks, covering their models, applications, datasets, benchmarks, challenges, and future directions, while providing links to the original PDFs and highlighting distinctions between small and large-scale graph learning.
The Youku AI team won the Media AI Alibaba Entertainment Video Object Segmentation Challenge by enhancing the STM model with a spatial‑constrained memory reader, ASPP‑HRNet refinement, ResNeSt‑101 backbone, and a multi‑stage training pipeline, while also devising an unsupervised framework that combines DetectoRS detection, HRNet mask refinement, STM‑based association, and key‑frame optimization to achieve 95.5% test score on a large, richly annotated video dataset.
User behavior sequence modeling, crucial for search and recommendation advertising ranking, has evolved from simple pooling to attention, RNN, capsule, and Transformer architectures, with industrial applications across e‑commerce, social, video, and music platforms, and future directions include time‑aware, multi‑dimensional, and self‑supervised approaches.
Amid the convergence of Qingming and Easter, this article examines how AI technologies—from voice synthesis to digital avatars—are being used to preserve and “resurrect” deceased loved ones, exploring real-world examples, technical methods, ethical dilemmas, and the future potential of digital immortality.
This tutorial explains how to install, launch, and use TensorBoard and Visdom with PyTorch, providing step‑by‑step commands, code examples for logging training metrics, and visualizing images and plots to monitor deep‑learning experiments.
iQIYI’s OCR journey, explained by researcher Harlon, covers the evolution from separate detection and recognition pipelines to end‑to‑end models, key algorithms like CTPN, DB and CRNN, large‑scale simulated training, diverse video‑text applications, and future goals such as mobile deployment and tighter NLP integration.
Researchers at Northwestern University have developed the Tri‑AI system, a deep‑learning facial recognition platform that accurately identifies individual golden snub‑nosed monkeys in the wild, achieving 94% precision and enabling non‑invasive monitoring, data collection, and broader applications across multiple animal species.
The paper introduces UMEC, a unified model compression framework that jointly optimizes feature embedding and prediction modules under resource constraints, achieving up to three‑fold compression of recommendation models without sacrificing accuracy, and demonstrates superior performance on multiple benchmark datasets.
Amap’s visual‑technology pipeline automatically generates and updates POI names by crowdsourcing street‑level images, applying deep‑learning scene‑text recognition, dual‑branch classification of text attributes, and a BERT‑plus‑graph‑attention model that selects and orders recognized text, achieving about 95 % naming accuracy.
This article presents a comprehensive overview of Taobao Live's deep ranking system evolution, detailing the DBMTL multi‑task learning framework, the two‑tower DMR matching‑ranking architecture, and the RUI Ranking refer‑item model, together with their offline formulas, online deployment scenarios, and measured performance gains across click‑through, watch‑time, and conversion metrics.
This article reviews recent research on medical sentiment analysis, covering interpretability of neural models, contextual aspect‑sentiment interactions, strategies for handling noisy labels, and methods for building domain‑specific sentiment lexicons, highlighting challenges and proposed solutions.
This article explains the architecture and ranking process of modern recommendation systems, covering the two-stage pipeline of candidate generation and ranking, the evolution from rule‑based methods to logistic regression, GBDT, wide‑and‑deep, and deep learning models, and discusses challenges such as feature non‑linearity, multi‑objective optimization, and the need for post‑ranking interventions.
This article summarizes a AAAI 2021 paper that introduces a deep meta‑learning framework with an amortization network to generate spatio‑temporal representations, enabling accurate retail sales predictions across regions and time periods, especially during high‑volume shopping festivals.
PyTorch 1.8, released by the PyTorch team, bundles over 3,000 commits since 1.7, introducing AMD ROCm support, enhanced Python function conversion, stable FFT and linear‑algebra APIs, complex‑tensor autograd, distributed‑training improvements, new mobile tutorials, performance tools, and several prototype features.
This article examines why accelerating cloud‑based deep learning is essential, presents benchmark results comparing GPU generations and distributed training, introduces Alluxio as a distributed memory‑level cache, details its architecture on Kubernetes, and offers concrete tuning strategies to overcome I/O bottlenecks and boost training performance.
WeChat’s 8.0 update introduced an OCR pipeline that first quickly detects text in images, classifies the image type, applies a lightweight multi‑language detection network and a MobileNetV3‑based DBNet recognizer with a multi‑task CTC/Attention model, then merges results via a rule‑based layout analyzer to deliver accurate, well‑formatted extracted text across diverse languages and document types.
This article presents the design and implementation of a multi‑objective optimization framework using Multi‑gate Mixture‑of‑Experts (MMoE) to improve click‑through, conversion, and purchase behaviors in Taobao's "Lying Flat" home‑goods recommendation channel, detailing model variants, feature engineering, loss weighting, and online A/B test results.
This article introduces the concepts, tasks, and recent advances in text sentiment analysis, focusing on attribute‑level sentiment (TG‑ABSA) and opinion‑quadruple extraction, describing unsupervised, reading‑comprehension, and multi‑task deep‑learning approaches, their implementation on Huawei Cloud, experimental results, and future research directions.
This article details the AI-driven intelligent voice quality inspection system built by 58.com, covering its background, multi‑layer architecture, speech recognition, role and tag identification, backend services, and the resulting efficiency gains for large‑scale call‑center operations.
This article details Kuaishou Y‑tech’s Gorgeous beauty platform, covering traditional smoothing, advanced skin‑tone effects, AI‑driven blemish removal, clarity enhancement, local facial tuning, and the UNet‑based GorgeousGAN that delivers one‑click high‑definition beauty for live‑stream and short‑video applications.
This article explains Alibaba's Deep Tree Matching (TDM) technology, covering the challenges of large‑scale match retrieval, the progression from classic two‑stage recall to tree‑based indexing, max‑heap tree modeling, beam‑search retrieval, and the joint model‑index learning across TDM 1.0, 2.0, and 3.0, highlighting significant offline and online performance gains and future research directions.
This article details how 58.com tackled the challenges of multi‑business recommendation on its home page by developing a dual‑channel deep interest model, introducing customized feature‑crossing, optimizing training and online performance, and exploring multi‑channel extensions for broader scenario adaptation.
This article describes how 58.com’s rental platform leverages deep learning models such as Wide&Deep, DeepFM, DCN, DIN, and DIEN to improve search ranking, detailing data pipelines, feature engineering, model iteration, multi‑task training, prediction optimizations, and resulting online performance gains.
This guide walks you through installing NVIDIA drivers, CUDA, and nvidia-docker2 on a Linux host and configuring Docker containers to access the GPU, including verification steps and sample TensorFlow and PyTorch commands.
The article introduces the CA‑TCN model, which combines cross‑session item graphs, a temporal convolutional network, and a session‑context graph to capture both item‑level and session‑level cross‑session influences, achieving state‑of‑the‑art performance on benchmark session‑based recommendation datasets.
The lecture presented by Assistant Professor Yanzhi Wang introduces a compression‑compilation co‑design framework (CoCoPIE) that achieves real‑time deep‑learning inference on edge devices through novel pruning and quantization techniques, delivering up to 180× speedup without accuracy loss.
The AMAP‑TECH competition challenged participants to infer real‑time road conditions from in‑vehicle video, prompting the authors to combine lane‑wise vehicle detection with LightGBM and later an end‑to‑end DenseNet‑GRU model, augment data, ensemble five networks, and achieve a 0.7237 F1 score while outlining future deployment and research directions.
This article introduces a hierarchical graph neural network model that jointly captures multi‑scale temporal patterns and global spatial context for urban traffic flow prediction, demonstrates its superiority over existing methods on multiple public datasets, and validates each component through extensive ablation studies.
This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.
This article presents an overview of graph neural networks, explains their computational framework, analyzes space and time complexities, and proposes ten practical strategies—including edge avoidance, dimensionality reduction, selective iteration, memory baking, distillation, partitioning, sparse computation, routing, and cross-sample feature sharing—to significantly lower the cost of large‑scale GNN processing.
The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.
This article explains what OCR is, introduces the EasyOCR Python library, shows how to install it, walks through step‑by‑step usage with code examples, and summarizes the underlying deep‑learning techniques powering the library.
This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.
This article reviews four mainstream model compression and acceleration methods—structural optimization, pruning, quantization, and knowledge distillation—explaining their principles, implementations, and performance, and presents practical examples such as DistillBERT, TinyBERT, and FastBERT with comparative results.
The article presents Alibaba's display‑advertising team’s three‑stage computing‑power efficiency evolution, introduces the DCAF personalized power‑allocation algorithm with its Lagrangian formulation, and describes the AllSpark dynamic‑control framework that together enable a flexible, resource‑aware Transformers engine achieving significant business gains during high‑traffic events.
The article traces L4 autonomous-driving perception from early rule-based point-cloud methods through data-driven deep-learning models to emerging self-learning, multi-task systems, and highlights four key hurdles—model generalization and explainability, robust multi-sensor fusion, real-time compute limits, and proper uncertainty handling—calling for integrated AI, engineering, and data solutions.
The paper proposes a deep meta‑learning framework that generates spatio‑temporal representations for retail sales forecasting, especially during large shopping festivals, by combining amortization networks, shared statistical structures, and alternating spatial‑temporal training to achieve robust and accurate predictions despite scarce historical data.
tinygrad, an open‑source autograd tensor library by George Hotz, offers a compact PyTorch‑like experience in fewer than 1000 lines, with easy installation, GPU support via PyOpenCL, full EfficientNet inference, and extensible optimizers for rapid neural‑network prototyping.
Learn how to generate cinematic ghosting effects in videos by leveraging PaddlePaddle’s PaddleHub deep learning library and the pretrained deeplabv3p_xception65 model for semantic segmentation, with step‑by‑step code, environment setup, and practical testing on classic martial‑arts footage.
The article presents a comprehensive study of re‑ranking in large‑scale industrial recommendation pipelines, identifies four key challenges—context awareness, permutation specificity, computational complexity, and business constraints—and proposes a two‑stage List Retrieval System that combines fast sequence search and a generative re‑ranking network with a deep context‑wise model, achieving significant online gains across multiple Taobao feed scenarios.
This article introduces LightSeq, an open‑source, GPU‑accelerated inference engine that dramatically speeds up Transformer‑based models such as BERT and GPT by up to 14× over TensorFlow, supports multiple decoding strategies, integrates seamlessly with major deep‑learning frameworks, and provides detailed performance benchmarks and technical optimizations.
Tencent’s open‑source Game AI SDK provides a versatile automation testing platform—supporting a wide range of game genres and mobile/PC apps—by integrating environment simulation, configurable tools, image‑recognition modules, and deep‑learning algorithms (DQN and IM) into a unified, user‑friendly workflow for training and executing AI agents.
The Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) combines a cross‑session item graph, a dilated temporal convolutional network, and a session‑context graph to capture both global cross‑session signals and positional order, achieving state‑of‑the‑art recommendation performance on benchmarks and slated for deployment in Meituan’s e‑commerce platforms.
This article explores how AI is being applied to waste classification—from smart trash cans and autonomous garbage trucks to deep‑learning models—while highlighting data‑labeling hurdles, model selection pitfalls, and how platforms like Huawei Cloud ModelArts can streamline development.
This article introduces the AI‑powered "Bringing-Old-Photos-Back-to-Life" project, explains its requirements, provides step‑by‑step commands for full‑pipeline restoration, scratch detection, global restoration, and face enhancement, and shares the Colab demo and GitHub repository for hands‑on experimentation.
This article provides a comprehensive overview of Generative Adversarial Networks, covering their original formulation, training dynamics, loss functions, major variants such as DCGAN and WGAN, and practical implementations for image‑to‑image translation, style transfer, and handwriting font synthesis at Laiye Technology.
Y‑Tech’s in‑house keypoint detection system powers Kuaishou’s beauty and effect filters across live streaming, video creation, and editing by leveraging lightweight deep‑learning models, extensive multi‑scenario data collection, and specialized handling of occlusion, enabling real‑time, robust facial and body landmark tracking on diverse mobile devices.
This article reviews common image similarity detection techniques—including hash-based methods (aHash, pHash, dHash), histogram comparison, feature matching with ORB and SIFT/SURF, bag‑of‑words with K‑Means, and CNN‑based VGG16 features—detailing their algorithms, Python implementations, performance characteristics, and practical considerations.
This article presents a deep‑learning Seq2Seq model that converts images of mathematical formulas—including matrices, equations, fractions, and radicals—into LaTeX sequences with over 95% accuracy, detailing data preparation, LaTeX normalization, model architecture, training, inference, and post‑processing techniques.
This article introduces NVIDIA's Merlin recommendation system framework, detailing its three core components—NVTabular for feature engineering, HugeCTR for high‑performance CTR model training, and Triton for inference—while discussing common pipeline challenges, performance advantages, and example implementations for deep‑learning‑based recommender models.
iQIYI’s AI‑driven coloring engine uses a GAN‑based model with attention and semantic reference modules to automate line‑art coloring, delivering consistent styles across frames, cutting coloring time to 0.7 seconds per image and reducing labor costs by about 30% in animation pipelines.
This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.
Dewu Photo Search evolved from a limited Aliyun‑based prototype to a self‑developed pipeline using EfficientNet detection and 128‑dim embeddings, boosting top‑1 shoe accuracy over 100 % and overall precision by up to 41 %, while reducing latency and improving scalability despite remaining stability challenges.
The presentation by Alibaba Entertainment's senior algorithm expert details the challenges of video search in the 4G/5G era and describes a comprehensive framework covering business overview, relevance and ranking, multimodal retrieval, deep semantic modeling, dataset construction, and practical deployment techniques.
This lecture details the end‑to‑end AI architecture for unmanned stores, covering algorithm module selection, calibration, face recognition, multi‑task detection, tracking, recommendation, data collection, augmentation, model training, and GPU‑accelerated deployment to achieve real‑time performance and high accuracy.
This article explains how a cloud‑based trade fair leverages AI techniques—including user and item profiling, multi‑level caching with Caffeine and Redis, and a Deep Interest Network model with attention mechanisms—to deliver personalized, high‑performance recommendations for exhibitors, buyers, and individual users.
The article surveys recent advances in single‑channel speech separation and target‑speaker extraction, explains the encoder‑separator‑decoder framework, compares frequency‑ and time‑domain methods, highlights models such as SpEx+, DPRNN‑Spe, and introduces Iterative Refined Adaptation, which iteratively improves speaker embeddings to boost SI‑SDR performance and enables effective speaker‑suppression for applications like in‑vehicle voice interaction.
This article investigates how to dynamically identify road closures by classifying vehicle, bicycle, and pedestrian trajectories, addressing sample imbalance and noisy labels with a label‑probability mixture Bayesian model and a deep‑learning image‑encoding pipeline, and compares their experimental results.
This lecture outlines the key challenges of deep learning in retail—growing model size, speed, and energy consumption—and presents a comprehensive acceleration framework covering algorithmic optimizations like network design, pruning, and hardware acceleration, with practical examples such as MobileNet, model compression, and edge deployment.
This article discusses a deep spatial-temporal tensor factorization framework for large-scale user visits understanding and forecasting, addressing challenges in advertising inventory prediction and demonstrating significant improvements over traditional methods.
This paper proposes a novel spatio‑temporal Siamese network that identifies whether GPS trajectories belong to the same driver, addressing data‑cost and scalability issues of existing methods and demonstrating superior accuracy and generalization on large‑scale taxi driver datasets.
FrameX is an AI-powered platform developed by Beike’s Data Intelligence Center that leverages vector floorplan data to automatically tag, score, interpret, cluster, and retrieve housing layouts, supporting numerous business scenarios through a layered architecture of data, feature, and application layers.
FinBERT 1.0 is an open‑source Chinese BERT model pre‑trained on large‑scale financial corpora that achieves 2‑5 % F1 improvements across multiple downstream fintech tasks without additional tuning, demonstrating the value of domain‑specific pre‑training for natural language processing.
This article presents DeepMatch, a behavior‑sequence based deep learning recall model enhanced with play‑rate and intent‑type embeddings, describes its self‑attention architecture, factorized embedding parameterization, multitask loss design, distributed TensorFlow training tricks, and demonstrates significant offline and online improvements in music recommendation performance.
