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ICLR

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Research Like Stock Trading: 40K ICLR Papers Show Chasing Hot Topics Is Rational — But Alpha Decays Fast

Analysis of 42,123 ICLR papers (2017–2026) reveals that chasing hot research topics yields higher acceptance rates initially, but the advantage vanishes as fields become crowded; for PhD students with short time horizons, following momentum is rational, but growth increasingly goes into rejections rather than acceptances.

ICLRPhD strategyacceptance rates
0 likes · 14 min read
Research Like Stock Trading: 40K ICLR Papers Show Chasing Hot Topics Is Rational — But Alpha Decays Fast
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Sep 11, 2026 · Artificial Intelligence

Research Like Stock Trading: 40K ICLR Papers Backtest Shows Chasing Hot Topics Works

Analyzing 42,123 ICLR papers (2017–2026) across 28 research directions, the study finds that while hot topics like LLMs grow 60× in three years, their acceptance-rate advantage vanishes at peak popularity; PhD students with short horizons rationally chase momentum, but must check whether growth translates to acceptances or rejections.

AgentGNNICLR
0 likes · 14 min read
Research Like Stock Trading: 40K ICLR Papers Backtest Shows Chasing Hot Topics Works
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 25, 2026 · Artificial Intelligence

ICLR 2026 Award Winners: Outstanding Papers and Alec Radford’s Test‑of‑Time Honor

ICLR 2026 announced two Outstanding Paper awards, a Honorable Mention, and two Test‑of‑Time awards—including the seminal DCGAN and DDPG papers—highlighting a 19,000‑paper submission pool with a 28% acceptance rate and showcasing new theoretical insights on Transformers and multi‑turn LLM evaluation.

DCGANDDPGICLR
0 likes · 8 min read
ICLR 2026 Award Winners: Outstanding Papers and Alec Radford’s Test‑of‑Time Honor
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 21, 2026 · Artificial Intelligence

Why Do Papers with a '?' in the Title Achieve a 45% Acceptance Rate? A Five‑Year ICLR Keyword Analysis

Analyzing five years of ICLR submission metadata reveals that titles containing a question mark boost acceptance to 45.5% in 2022, while emerging keywords such as diffusion, sparse, and planning dominate high‑acceptance lists, and older topics like federated learning, adversarial attacks, and security suffer low acceptance and high withdrawal rates.

Data AnalysisICLRNatural Language Processing
0 likes · 8 min read
Why Do Papers with a '?' in the Title Achieve a 45% Acceptance Rate? A Five‑Year ICLR Keyword Analysis
Machine Heart
Machine Heart
Apr 20, 2026 · Artificial Intelligence

ICLR Retracts Oral Acceptance Over Sanctions: Where Is Academic Freedom?

A paper initially accepted for an oral presentation at ICLR was later desk‑rejected after program chairs discovered the work was affiliated with RAIRI, a Russian AI institute now on the US OFAC sanctions list, sparking debate over academic freedom and conference compliance.

AI conferencesICLRacademic freedom
0 likes · 6 min read
ICLR Retracts Oral Acceptance Over Sanctions: Where Is Academic Freedom?
DevOps
DevOps
Apr 9, 2025 · Artificial Intelligence

AI Scientist v2 Generates ICLR Workshop Paper Reviewed and Accepted

An AI‑generated research paper created entirely by Sakana AI’s AI Scientist‑v2 system achieved a 6/7/6 score and passed peer review at an ICLR workshop, demonstrating end‑to‑end hypothesis generation, experiment execution, data analysis, and manuscript writing, while highlighting the system’s capabilities and limitations.

AI scientistAI-generated researchAgentic Tree Search
0 likes · 8 min read
AI Scientist v2 Generates ICLR Workshop Paper Reviewed and Accepted
JD Retail Technology
JD Retail Technology
Mar 6, 2025 · Artificial Intelligence

Dynamic Margin Selection for Efficient Deep Learning and Low-Resource Large Model Training

Jia Xing’s research introduces Dynamic Margin Selection, a technique that repeatedly refreshes a core set of boundary‑close samples to train large language models efficiently on limited resources, achieving comparable loss to full‑data training, enabling six‑fold model compression, faster inference, and a proposed exponential scaling law for data‑efficient AI.

ICLRLow-Resource Trainingdynamic data selection
0 likes · 10 min read
Dynamic Margin Selection for Efficient Deep Learning and Low-Resource Large Model Training
DataFunSummit
DataFunSummit
Oct 9, 2021 · Artificial Intelligence

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Solving Generality and Over‑Smoothing in Graph Neural Networks

This article presents the Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN), explains the two main limitations of existing GNNs—lack of generality across homophilic and heterophilic graphs and the over‑smoothing problem—and demonstrates through synthetic and real‑world experiments that GPR‑GNN achieves robust node classification while remaining interpretable and parameter‑efficient.

GPR-GNNICLROver‑smoothing
0 likes · 18 min read
Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Solving Generality and Over‑Smoothing in Graph Neural Networks