The article explains how manufacturers can shift from defect‑specific vision models to anomaly detection by leveraging pre‑trained object‑detection networks, visualising feature maps, and applying memory‑bank methods such as PaDiM and PatchCore, with the open‑source Anomalib library as a ready‑to‑use solution.
This article analyzes the current limitations of Graph Prompt Learning, reveals that representation‑level prompts are essentially equivalent to fine‑tuning a downstream classifier, and introduces UniPrompt—a method that leverages pre‑trained graph models while preserving input graph structure for superior in‑domain and cross‑domain performance.
This comprehensive guide walks you through the evolution of AI model architectures—from CNNs and RNNs to Transformers and GANs—explaining their core concepts, typical use cases, and how to select, train, and fine‑tune pre‑trained models using practical code examples.
The article introduces PreFLMR, an open‑source, general‑purpose pre‑trained multimodal retriever that leverages fine‑grained late‑interaction to boost retrieval‑augmented generation for knowledge‑intensive visual tasks, describes its M2KR benchmark, training stages, and strong experimental results across multiple tasks.
The IEEE ICDM 2023 Graph Learning Challenge, co‑hosted by Ant Group and Zhejiang University, showcased deep graph learning approaches for community detection and fraud‑group mining, highlighting the winning team's Risk‑DCRN method and emphasizing the importance of pretrained models in large‑scale network analysis.
This article presents an end‑to‑end overview of Alibaba Cloud’s Machine Learning PAI platform, detailing the three‑stage ML workflow, challenges in model development, the role of pre‑trained and open‑source models, PAI’s architecture, a hands‑on demo, and MLOps best practices for efficient model deployment.
This article reviews the rapidly evolving field of structured‑data text generation, covering AI development stages, core concepts, model architectures from pipeline to pretrained transformers, key challenges such as content selection, numeric representation, reasoning and style control, and outlines recent research directions and Q&A insights.
This article introduces the tasks, techniques, and frameworks for financial event analysis using pre‑trained language models, covering unstructured data parsing, event semantics, graph construction, detection, extraction, and prediction, and presents the TDE‑GTEE model that achieves state‑of‑the‑art performance even in few‑shot scenarios.
This article shares the development of a global search platform that leverages AI technologies such as Chinese word segmentation, part‑of‑speech tagging, text similarity via Simhash and Synonyms, image similarity using histogram, Hamming distance and ResNet‑50, and multimodal CLIP‑based models to improve search efficiency and accuracy.
This article explains how Alibaba's enterprise search system tackles query intent recognition by combining knowledge‑enhanced techniques, short‑text classification, and pretrained language models such as StructBERT and prompt‑learning, and it shares two real‑world case studies, experimental results, and future research directions.
This article reviews the evolution of NLP training paradigms, explains why prompt learning is needed, defines its core concepts, and surveys major hard‑template and soft‑template methods such as PET, LM‑BFF, P‑tuning, and Prefix‑tuning, highlighting their advantages for few‑shot and zero‑shot scenarios.
PGLBox, Baidu’s GPU‑based large‑scale graph training framework, delivers up to 27× speedup over CPU clusters by fully GPU‑accelerating storage, sampling, and training, supporting billions of nodes, advanced GNN algorithms, multi‑level storage, and seamless integration of massive pretrained models.
Xiaohongshu’s end‑to‑end intelligent creative platform extracts high‑quality images, generates diverse titles with RED‑pretrained GPT‑2/T5 models, and selects the best ads using a UCB‑based multi‑armed bandit that balances CTR uplift, revenue and user‑experience, while employing position‑corrected metrics and a scalable dual‑tower DNN to boost long‑tail performance and overall revenue.
This article presents a comprehensive overview of industry search, covering its background, core retrieval and ranking technologies—including sparse and dense retrieval, pre‑trained language models, tokenization, NER, adaptive multi‑task training, and re‑ranking models—followed by detailed case studies such as address analysis, family‑ID unification, emergency call handling, education photo‑search, and power‑knowledge‑base integration.
This article explains how Baidu's anti‑spam team tackled large‑scale promotional spam on Baidu Zhidao by combining the Ernie pretrained model with SimCSE contrastive learning, detailing the problem background, traditional methods, text‑representation stages, the SimCSE approach, training pipeline, optimizations, and experimental results.
This article walks readers through building, training, and evaluating a Vision Transformer (ViT) model for a five‑class flower classification task, providing detailed code snippets, model architecture explanations, training script adjustments, and experimental results that highlight the importance of pre‑trained weights.
This tutorial walks you through NLP fundamentals, the evolution of BERT, the concept of pre‑trained models, and a step‑by‑step guide to fine‑tune a Chinese BERT on a cloze‑style task, complete with code snippets and verification results.
The article reviews how the Transformer breakthrough, the rapid scaling of large language models such as GPT‑3, Switch Transformer, and Alibaba's AliceMind and M6, together with multimodal research, are shaping the next phase of artificial intelligence toward more general, collaborative, and open AI systems.
Ant Group's insurance technology team secured the top spot in the fine‑grained dialogue social bias detection task at the 11th CCF NLPCC conference, showcasing their AI‑driven bias‑mitigation methods, a proprietary pre‑trained model AntInsBert, and a claim‑automation system that boosts insurance service fairness and efficiency.
This article reviews Huawei Noah's Ark Lab's work on modern information‑flow recommendation, covering the evolution from collaborative filtering to deep learning, the application of BERT‑based pre‑training for news ranking, multimodal user‑interface modeling, practical deployment challenges, and future research directions.
This article reviews recent developments in text classification and intent recognition, comparing industrial practices such as business‑coupled feature engineering with research trends like pretrained language models, and provides references and practical insights for building effective NLP solutions.
This article examines how startups can harness massive pre‑trained AI models such as GPT‑3, outlining the historical context, benefits of transfer learning, the steep costs and data‑alignment challenges, and strategic considerations when using cloud APIs versus self‑hosting.
This article presents a comprehensive overview of Taobao's user‑generated content (UGC) sentiment analysis pipeline, covering background, task definition, challenges, model architecture—including RoBERTa‑based extraction, sentiment‑knowledge pre‑training, and graph augmentation—personalized impression ranking, business impact cases, and future research directions.
This article presents a comprehensive overview of Taobao's user‑generated content sentiment analysis pipeline, covering task definition, challenges, model architecture with RoBERTa‑based extraction, sentiment‑knowledge pre‑training, graph augmentation, personalized ranking, business impact metrics, and future research directions.
DeltaLM is a multilingual pretrained encoder‑decoder model that leverages cross‑lingual transfer from a pretrained encoder and novel decoder architecture, employs span‑corruption and translation‑pair pretraining tasks, and uses a two‑stage fine‑tuning strategy to achieve strong zero‑shot and supervised translation performance across over 100 languages.
This comprehensive review by Dr. Fan Yixing surveys how pretrained language models have transformed first‑stage information retrieval, tracing the shift from traditional term‑based methods to neural sparse, dense, and hybrid approaches, and discussing key challenges such as hard‑negative mining, joint indexing‑representation learning, and generative‑discriminative training.
Meituan’s NLP Center introduced the ASAP dataset—the largest real‑world Chinese attribute‑level sentiment corpus—to date, and the article traces the progression from document‑level regression models upgraded with MT‑BERT, through multi‑task attribute‑level ABSA and opinion‑triplet extraction, to scalable real‑time and batch services, while outlining future transfer‑learning and few‑shot research.
This article details the authors' approach to the 2021 Sohu Text Matching competition, describing the task definition, data splits, model architectures (cross‑encoder and bi‑encoder), pretrained language models used, various training tricks, ensemble strategies, and the resulting evaluation scores.
This article presents a comprehensive study of text representation techniques—from weighted word‑vector methods to supervised SimBert and unsupervised contrastive learning models—applied to large‑scale unstructured data in 58's information‑security workflows, evaluating their effectiveness for classification and content‑recall tasks.
This article surveys the evolution of pre‑trained models, covering the origins of transfer and self‑supervised learning, the rise of transformer‑based PTMs such as BERT and GPT, efficient architecture designs, multimodal and multilingual extensions, theoretical analyses, and future research directions for scalable and robust AI systems.
In a detailed interview, Huawei Cloud experts explain how the ultra‑large Pangu CV and NLP models—trained on billions of parameters and terabytes of data—achieve top benchmark scores, simplify developer workflows, and deliver industry‑wide deployments that dramatically cut labeling effort and iteration time.
In an InfoQ interview, JD Technology senior algorithm scientist Wu Youzheng reviews the rapid advances of natural language processing in 2020—including GPT‑3, multimodal dialogue, knowledge‑enhanced pre‑training, and knowledge graphs—while outlining the most promising research directions and practical challenges for the coming year.
The paper introduces QDGAT, a question‑directed graph attention network that enhances numerical reasoning in reading comprehension by explicitly modeling relationships among numbers, entities, and questions, and demonstrates its effectiveness through extensive experiments on the DROP dataset.
This article shares the Xiaomi AI Lab NLP team's experience in the NLPCC lightweight language model competition, discusses efficiency challenges of large pretrained models like BERT, and details practical inference optimizations—including model distillation, batching, FP16 quantization, and FasterTransformer integration—that dramatically reduce latency and hardware costs in production.
This article reviews how large‑scale pretrained language models have reshaped NLP, outlines the challenges of applying them to machine translation, introduces the APT framework and the GRET architecture for better encoder‑decoder integration, and reports experimental gains and future research directions.
This article provides a detailed survey of pre‑trained models (PTMs) for natural language processing, classifying them into shallow embeddings and contextual encoders, discussing training paradigms such as knowledge integration and model compression, and offering guidance on transfer learning and future challenges.
On August 6, Dr. Feng Ao presented a comprehensive overview of the evolution of word representations and sentiment analysis, illustrating the shift from traditional linguistic features to modern pretrained models such as BERT and XLNet, and sharing practical convolutional experiments relevant to industry applications.
