LeArning Abstract

Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.

2549papers

Papers - Page 72

May 28, 2024

From Learning to Optimize to Learning Optimization Algorithms
Camille Castera, Peter Ochs
Classical Algorithm L BFGS Optimization Algorithm LeArning Abstract Classical Optimization Learned Optimizers
Learning to Detour: Shortcut Mitigating Augmentation for Weakly Supervised Semantic Segmentation
JuneHyoung Kwon, Eunju Lee, Yunsung Cho, YoungBin Kim
Pixel Level Weakly Supervised Semantic Segmentation Efficient Augmentation Semantic Segmentation LeArning Abstract Background Generation

May 27, 2024

Learning with User-Level Local Differential Privacy
Puning Zhao, Li Shen, Rongfei Fan, Qingming Li, Huiwen Wu, Jiafei Wu, Zhe Liu
User Level Differential Privacy Privacy Level LeArning Abstract Information Theoretic
Hawk: Learning to Understand Open-World Video Anomalies
Jiaqi Tang, Hao Lu, Ruizheng Wu, Xiaogang Xu, Ke Ma, Cheng Fang, Bin Guo, Jiangbo Lu, Qifeng Chen, Ying-Cong Chen
Video Anomaly Detection Video Anomaly Motion Modality LeArning Abstract

May 24, 2024

Learning accurate and interpretable tree-based models
Maria-Florina Balcan, Dravyansh Sharma
Decision Tree LeArning Abstract Bayesian Decision Tree
Learning the Language of Protein Structure
Benoit Gaujac, Jérémie Donà, Liviu Copoiu, Timothy Atkinson, Thomas Pierrot, Thomas D. Barrett
Protein Model Protein Design Vector Quantized Variational Autoencoder Human Language Protein Structure LeArning Abstract
Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Vladislav Trifonov, Alexander Rudikov, Oleg Iliev, Yuri M. Laevsky, Ivan Oseledets, Ekaterina Muravleva
Conjugate Gradient Neural Preconditioners Adaptive Preconditioner Iterative Solver LeArning Abstract Linear Algebra
Learning to Discretize Denoising Diffusion ODEs
Vinh Tong, Hoang Trung-Dung, Anji Liu, Guy Van den Broeck, Mathias Niepert
Time Discretization Discrete Denoising Diffusion Pre Trained Diffusion Model LeArning Abstract Diffusion Probabilistic Model Generative Model
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal, Ferenc Huszár, Bernhard Schölkopf
Medical LLM Domain Knowledge Knowledge Structure Language Model Context Learning LeArning Abstract Mathematical Understanding
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao, Yuxing Han, Lixin Fan, Qiang Yang
Harmful Unlearning Federated Unlearning Machine Learning Paradigm LeArning Abstract
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Xiaohan Chen, Jialin Liu, Wotao Yin
Tutorial Review Traditional Optimization Real World Optimization Problem Optimization Algorithm LeArning Abstract Optimization Problem Mixed Integer
Learning from True-False Labels via Multi-modal Prompt Retrieving
Zhongnian Li, Jinghao Xu, Peng Ying, Meng Wei, Xinzheng Xu
Weakly Supervised Learning Label Noise LeArning Abstract Pre Trained Vision Language Model

May 23, 2024

LOVA3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng Shou
Multimodal Large Language Model Multimodal Comprehension Multimodal Benchmark Question Answering LeArning Abstract Direct Assessment Visual Question Answering
MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
Yihong Sun, Bharath Hariharan
Object Detection Segment Object Mobile Object Object Segmentation Unlabeled Video Motion Segmentation LeArning Abstract
Learning Multi-dimensional Human Preference for Text-to-Image Generation
Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, Zhongyuan Wang
Multi Dimensional Preference Score Text to Image Model Text to Image Generation LeArning Abstract
Learning with Fitzpatrick Losses
Seta Rakotomandimby, Jean-Philippe Chancelier, Michel de Lara, Mathieu Blondel
Cheeger Inequality Convex Loss Function Loss Function LeArning Abstract
LARS-VSA: A Vector Symbolic Architecture For Learning with Abstract Rules
Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee
Relational Representation Vector Symbolic Architecture Dimensional Attention Rule Abstraction LeArning Abstract Concept Bottleneck Model Neuro Symbolic Architecture
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin
Policy Evaluation Policy Selection Contextual Bandit Problem Importance Weight Importance Estimation View Selection Exponential Smoothing LeArning Abstract

LeArning Abstract

Papers - Page 72

From Learning to Optimize to Learning Optimization Algorithms

Learning to Detour: Shortcut Mitigating Augmentation for Weakly Supervised Semantic Segmentation

Learning with User-Level Local Differential Privacy

Hawk: Learning to Understand Open-World Video Anomalies

Learning to Reason via Program Generation, Emulation, and Search

Learning Visual-Semantic Subspace Representations

Learning accurate and interpretable tree-based models

Learning the Language of Protein Structure

Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers

Learning to Discretize Denoising Diffusion ODEs

Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs

Unlearning during Learning: An Efficient Federated Machine Unlearning Method

Learning to optimize: A tutorial for continuous and mixed-integer optimization

Learning from True-False Labels via Multi-modal Prompt Retrieving

LOVA3: Learning to Visual Question Answering, Asking and Assessment

MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos

Learning Multi-dimensional Human Preference for Text-to-Image Generation

Learning with Fitzpatrick Losses

LARS-VSA: A Vector Symbolic Architecture For Learning with Abstract Rules

Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning