title stringlengths 12 235 | full_text stringlengths 58 1.17M | summary stringlengths 99 1.95k | page_count float64 2 649 | cleaned_text stringlengths 58 746k |
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Online Weighted Paging with Unknown Weights | Online Weighted Paging with Unknown Weights
Orin Levy∗
Tel-Aviv University
orinlevy@mail.tau.ac.il
Noam Touitou
Amazon Science
noamtwx@gmail.com
Aviv Rosenberg†
Google Research
avivros007@gmail.com
Abstract
Online paging is a fundamental problem in the field of online algorithms, in which
one maintains a cache of 𝑘slo... | Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of $k$ slots as requests for fetching pages arrive online. In the weighted variant of this problem, each page has its own fetching cost; a substantial line of work on this problem culminated in an (optimal) $O(\\log... | 19 | Online Weighted Paging with Unknown Weights Orin Levy∗ Tel-Aviv University orinlevy@mail.tau.ac.il Noam Touitou Amazon Science noamtwx@gmail.com Aviv Rosenberg† Google Research avivros007@gmail.com Online weighted paging. In the online weighted paging problem, or OWP, one is given a cache of 𝑘slots, and requests for p... |
Modular Duality in Deep Learning | Modular Duality in Deep Learning
Jeremy Bernstein
jbernstein@mit.edu
Laker Newhouse
lakern@mit.edu
MIT CSAIL
Abstract
An old idea in optimization theory says that since the gradient is a dual vector it may not
be subtracted from the weights without first being mapped to the primal space where the
weights reside. We tak... | An old idea in optimization theory says that since the gradient is a dual vector it may not be subtracted from the weights without first being mapped to the primal space where the weights reside. We take this idea seriously in this paper and construct such a duality map for general neural networks. Our map, which we ca... | 12 | Modular Duality in Deep Learning Jeremy Bernstein jbernstein@mit.edu Laker Newhouse lakern@mit.edu MIT CSAIL In this paper, we pursue a rigorous and first-principles theoretical framework for designing neural network training algorithms. We hope that building such a framework will facilitate the design of a next genera... |
Adaptive Transfer Clustering: A Unified Framework | Adaptive Transfer Clustering: A Unified Framework
Yuqi Gu∗
Zhongyuan Lyu†
Kaizheng Wang‡
Abstract
We propose a general transfer learning framework for clustering given a main dataset and
an auxiliary one about the same subjects. The two datasets may reflect similar but different
latent grouping structures of the subjec... | We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the co... | 52 | Adaptive Transfer Clustering: A Unified Framework Yuqi Gu∗ Zhongyuan Lyu† Kaizheng Wang‡ Multiview clustering; Transfer learning; Adaptation; Bootstrap. 1 Introduction In recent years, data collection from multiple sources or views, each offering unique insights into the underlying structure, has become increasingly co... |
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference | BLAST: Block-Level Adaptive Structured Matrices for
Efficient Deep Neural Network Inference
Changwoo Lee
Soo Min Kwon
Qing Qu
Hun-Seok Kim
University of Michigan
{cwoolee,kwonsm,qingqu,hunseok}@umich.edu
Abstract
Large-scale foundation models have demonstrated exceptional performance in
language and vision tasks. Howev... | Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large networks pose significant computational challenges during inference. To address these challenges, we introduce the Block-Level Adaptive STruc... | 26 | BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference Changwoo Lee Soo Min Kwon Qing Qu Hun-Seok Kim University of Michigan {cwoolee,kwonsm,qingqu,hunseok}@umich.edu Foundation models built on large deep neural networks (DNNs) have demonstrated remarkable performance in vision and ... |
Quantum computing and persistence in topological data analysis | "arXiv:2410.21258v1 [quant-ph] 28 Oct 2024\nMIT-CTP/5802, YITP-24-131\nQuantum computing and persi(...TRUNCATED) | "Topological data analysis (TDA) aims to extract noise-robust features from a data set by examining (...TRUNCATED) | 21 | "arXiv:2410.21258v1 [quant-ph] 28 Oct 2024 MIT-CTP/5802, YITP-24-131 Quantum computing and persisten(...TRUNCATED) |
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation | "Preprint\nONE-STEP DIFFUSION POLICY: FAST VISUOMOTOR\nPOLICIES VIA DIFFUSION DISTILLATION\nZhendong(...TRUNCATED) | "Diffusion models, praised for their success in generative tasks, are increasingly being applied to (...TRUNCATED) | 18 | "Preprint ONE-STEP DIFFUSION POLICY: FAST VISUOMOTOR POLICIES VIA DIFFUSION DISTILLATION Zhendong Wa(...TRUNCATED) |
LongReward: Improving Long-context Large Language Models with AI Feedback | "LongReward: Improving Long-context Large Language Models\nwith AI Feedback\nJiajie Zhang1†, Zhong(...TRUNCATED) | "Though significant advancements have been achieved in developing long-context large language models(...TRUNCATED) | 21 | "LongReward: Improving Long-context Large Language Models with AI Feedback Jiajie Zhang1†, Zhongni(...TRUNCATED) |
Capacity-Aware Planning and Scheduling in Budget-Constrained Monotonic MDPs: A Meta-RL Approach | "Capacity-Aware Planning and Scheduling in Budget-Constrained Monotonic\nMDPs: A Meta-RL Approach\nM(...TRUNCATED) | "Many real-world sequential repair problems can be effectively modeled using monotonic Markov Decisi(...TRUNCATED) | 10 | "Capacity-Aware Planning and Scheduling in Budget-Constrained Monotonic MDPs: A Meta-RL Approach Man(...TRUNCATED) |
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback | "Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback\nNour Jedidi1\nYung-Sung Chuang2\(...TRUNCATED) | "Building effective dense retrieval systems remains difficult when relevance supervision is not avai(...TRUNCATED) | 15 | "Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback Nour Jedidi1 Yung-Sung Chuang2 Le(...TRUNCATED) |
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models | "Flaming-hot Initiation with Regular Execution Sampling for Large\nLanguage Models\nWeizhe Chen\nUni(...TRUNCATED) | "Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilitie(...TRUNCATED) | 9 | "Flaming-hot Initiation with Regular Execution Sampling for Large Language Models Weizhe Chen Univer(...TRUNCATED) |
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