paper_id uint32 0 3.26k | title stringlengths 15 150 | paper_url stringlengths 42 42 | authors listlengths 1 21 | type stringclasses 3
values | abstract stringlengths 393 2.58k | keywords stringlengths 5 409 | TL;DR stringlengths 7 250 ⌀ | submission_number int64 1 16.4k | arxiv_id stringlengths 10 10 ⌀ | embedding listlengths 768 768 | github stringlengths 26 123 ⌀ |
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0 | Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent | https://openreview.net/forum?id=2GmXJnyNM4 | [
"Santhosh Karnik",
"Anna Veselovska",
"Mark Iwen",
"Felix Krahmer"
] | Oral | We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. For matrix factorization problems, this phenomenon has been studied in a number of works. A particular challenge has been to design universal initialization strategies which pro... | overparameterization, implicit regularization, tensor factorization | We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. | 16,047 | 2410.16247 | [
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0.... | https://github.com/AnnaVeselovskaUA/tubal-tensor-implicit-reg-GD |
1 | Algorithm Development in Neural Networks: Insights from the Streaming Parity Task | https://openreview.net/forum?id=3go0lhfxd0 | [
"Loek van Rossem",
"Andrew M Saxe"
] | Oral | Even when massively overparameterized, deep neural networks show a remarkable ability to generalize. Research on this phenomenon has focused on generalization within distribution, via smooth interpolation. Yet in some settings neural networks also learn to extrapolate to data far beyond the bounds of the original train... | Out-of-distribution generalization, Algorithm discovery, Deep learning theory, Mechanistic Interpretability | We explain in a simple setting how out-of-distribution generalization can occur. | 16,013 | 2507.09897 | [
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2 | Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection | https://openreview.net/forum?id=GFpjO8S8Po | [
"Zhiyuan Yan",
"Jiangming Wang",
"Peng Jin",
"Ke-Yue Zhang",
"Chengchun Liu",
"Shen Chen",
"Taiping Yao",
"Shouhong Ding",
"Baoyuan Wu",
"Li Yuan"
] | Oral | Detecting AI-generated images (AIGIs), such as natural images or face images, has become increasingly important yet challenging. In this paper, we start from a new perspective to excavate the reason behind the failure generalization in AIGI detection, named the asymmetry phenomenon, where a naively trained detector ten... | AI-Generated Image Detection, Face Forgery Detection, Deepfake Detection, Media Forensics | We introduce a novel approach via orthogonal subspace decomposition for generalizing AI-generated images detection. | 15,222 | 2411.15633 | [
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0.0... | https://github.com/YZY-stack/Effort-AIGI-Detection |
3 | Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies | https://openreview.net/forum?id=vQubr1uBUw | [
"Nadav Timor",
"Jonathan Mamou",
"Daniel Korat",
"Moshe Berchansky",
"Gaurav Jain",
"Oren Pereg",
"Moshe Wasserblat",
"David Harel"
] | Oral | Accelerating the inference of large language models (LLMs) is a critical challenge in generative AI. Speculative decoding (SD) methods offer substantial efficiency gains by generating multiple tokens using a single target forward pass. However, existing SD approaches require the drafter and target models to share the s... | Speculative Decoding, Large Language Models, Vocabulary Alignment, Heterogeneous Vocabularies, Efficient Inference, Inference Acceleration, Rejection Sampling, Tokenization, Transformer Architectures, Text Generation, Open Source. | null | 15,148 | 2502.05202 | [
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0... | https://github.com/keyboardAnt/hf-bench |
4 | LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models | https://openreview.net/forum?id=SyQPiZJVWY | [
"Parshin Shojaee",
"Ngoc-Hieu Nguyen",
"Kazem Meidani",
"Amir Barati Farimani",
"Khoa D Doan",
"Chandan K. Reddy"
] | Oral | Scientific equation discovery is a fundamental task in the history of scientific progress, enabling the derivation of laws governing natural phenomena. Recently, Large Language Models (LLMs) have gained interest for this task due to their potential to leverage embedded scientific knowledge for hypothesis generation. Ho... | Benchmark, Scientific Discovery, Large Language Models, Symbolic Regression | We present LLM-SRBench, the first comprehensive benchmark for evaluating scientific equation discovery with LLMs, designed to rigorously assess discovery capabilities beyond memorization | 14,812 | null | [
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5 | ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features | https://openreview.net/forum?id=Rc7y9HFC34 | [
"Alec Helbling",
"Tuna Han Salih Meral",
"Benjamin Hoover",
"Pinar Yanardag",
"Duen Horng Chau"
] | Oral | Do the rich representations of multi-modal diffusion transformers (DiTs) exhibit unique properties that enhance their interpretability? We introduce ConceptAttention, a novel method that leverages the expressive power of DiT attention layers to generate high-quality saliency maps that precisely locate textual concepts ... | diffusion, interpretability, transformers, representation learning, mechanistic interpretability | We introduce a method for interpreting the representations of diffusion transformers by producing saliency maps of textual concepts. | 14,767 | 2502.04320 | [
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0.... | https://github.com/helblazer811/ConceptAttention |
6 | Emergence in non-neural models: grokking modular arithmetic via average gradient outer product | https://openreview.net/forum?id=36hVB7DEB0 | [
"Neil Rohit Mallinar",
"Daniel Beaglehole",
"Libin Zhu",
"Adityanarayanan Radhakrishnan",
"Parthe Pandit",
"Mikhail Belkin"
] | Oral | Neural networks trained to solve modular arithmetic tasks exhibit grokking, a phenomenon where the test accuracy starts improving long after the model achieves 100% training accuracy in the training process. It is often taken as an example of "emergence", where model ability manifests sharply through a phase transition... | Theory of deep learning, grokking, modular arithmetic, feature learning, kernel methods, average gradient outer product (AGOP), emergence | We show that "emergence" in the task of grokking modular arithmetic occurs in feature learning kernels using the Average Gradient Outer Product (AGOP) and that the features take the form of block-circulant features. | 14,743 | 2407.20199 | [
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0.0... | https://github.com/nmallinar/rfm-grokking |
7 | Hierarchical Refinement: Optimal Transport to Infinity and Beyond | https://openreview.net/forum?id=EBNgREMoVD | [
"Peter Halmos",
"Julian Gold",
"Xinhao Liu",
"Benjamin Raphael"
] | Oral | Optimal transport (OT) has enjoyed great success in machine learning as a principled way to align datasets via a least-cost correspondence, driven in large part by the runtime efficiency of the Sinkhorn algorithm (Cuturi, 2013). However, Sinkhorn has quadratic space complexity in the number of points, limiting scalabil... | Optimal transport, low-rank, linear complexity, sparse, full-rank | Linear-complexity optimal transport, using low-rank optimal transport to progressively refine the solution to a Monge map. | 14,649 | 2503.03025 | [
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0.... | https://github.com/raphael-group/HiRef |
8 | Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions | https://openreview.net/forum?id=DjJmre5IkP | [
"Jaeyeon Kim",
"Kulin Shah",
"Vasilis Kontonis",
"Sham M. Kakade",
"Sitan Chen"
] | Oral | In recent years, masked diffusion models (MDMs) have emerged as a promising alternative approach for generative modeling over discrete domains. Compared to autoregressive models (ARMs), MDMs trade off complexity at training time with flexibility at inference time. At training time, they must learn to solve an exponenti... | Discrete Diffusion models, Masked Diffusion Models, Diffusion Models, Learning Theory, Inference-Time Strategy | null | 14,095 | 2502.06768 | [
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9 | Statistical Test for Feature Selection Pipelines by Selective Inference | https://openreview.net/forum?id=4EYwwVuhtG | [
"Tomohiro Shiraishi",
"Tatsuya Matsukawa",
"Shuichi Nishino",
"Ichiro Takeuchi"
] | Oral | A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating various analysis algorithms. In this paper, we propose a novel statistical test to assess the significance of data analysis pipelines. Our approach enables the systematic development of valid stat... | Data Analysis Pipeline, AutoML, Statistical Test, Selective Inference, Missing Value Imputation, Outlier Detection, Feature Selection | We introduce a statistical test for data analysis pipeline in feature selection problems, which allows for the systematic development of valid statistical tests applicable to any pipeline configuration composed of a set of predefined components. | 13,925 | 2406.18902 | [
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0.000... | https://github.com/Takeuchi-Lab-SI-Group/si4pipeline |
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