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09/21/2023
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Large language models (LLMs) have pushed the limits of natural language ...
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08/15/2023
Forward-Backward Reasoning in Large Language Models for Verification
Chain-of-Though (CoT) prompting has shown promising performance in vario...
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06/01/2023
Effective Structured Prompting by Meta-Learning and Representative Verbalizer
Prompt tuning for pre-trained masked language models (MLM) has shown pro...
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05/18/2023
A Survey on Time-Series Pre-Trained Models
Time-Series Mining (TSM) is an important research area since it shows gr...
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09/27/2022
Searching a High-Performance Feature Extractor for Text Recognition Network
Feature extractor plays a critical role in text recognition (TR), but cu...
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02/17/2022
Revisiting Over-smoothing in BERT from the Perspective of Graph
Recently over-smoothing phenomenon of Transformer-based models is observ...
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09/17/2021
Dropout's Dream Land: Generalization from Learned Simulators to Reality
A World Model is a generative model used to simulate an environment. Wor...
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06/13/2021
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution
Recently, deep convolutional neural network methods have achieved an exc...
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06/13/2021
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Recently, convolutional neural network (CNN) based image super-resolutio...
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06/11/2021
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
In few-shot domain adaptation (FDA), classifiers for the target domain a...
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02/25/2021
SparseBERT: Rethinking the Importance Analysis in Self-attention
Transformer-based models are popular for natural language processing (NL...
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11/09/2020
A Survey of Label-noise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
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08/14/2020
Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization
Matrix learning is at the core of many machine learning problems. To enc...
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11/26/2019
Effective Decoding in Graph Auto-Encoder using Triadic Closure
The (variational) graph auto-encoder and its variants have been popularl...
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05/27/2019
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Communication overhead is a major bottleneck hampering the scalability o...
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05/23/2019
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Stochastic methods with coordinate-wise adaptive stepsize (such as RMSpr...
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03/08/2019
General Convolutional Sparse Coding with Unknown Noise
Convolutional sparse coding (CSC) can learn representative shift-invaria...
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06/08/2018
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Variance reduction has been commonly used in stochastic optimization. It...
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05/04/2018
Power Law in Sparsified Deep Neural Networks
The power law has been observed in the degree distributions of many biol...
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04/27/2018
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Convolutional sparse coding (CSC) has been popularly used for the learni...
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02/23/2018
Loss-aware Weight Quantization of Deep Networks
The huge size of deep networks hinders their use in small computing devi...
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10/19/2017
Scalable Robust Matrix Factorization with Nonconvex Loss
Robust matrix factorization (RMF), which uses the ℓ_1-loss, often outper...
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10/19/2017
Efficient Robust Matrix Factorization with Nonconvex Loss
Robust matrix factorization (RMF), which uses the ℓ_1-loss, often outper...
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08/01/2017
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Low-rank modeling has many important applications in computer vision and...
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06/21/2017
Scalable Online Convolutional Sparse Coding
Convolutional sparse coding (CSC) improves sparse coding by learning a s...
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04/04/2017
Multi-Label Learning with Global and Local Label Correlation
It is well-known that exploiting label correlations is important to mult...
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03/16/2017
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion
Matrix and tensor completion aim to recover a low-rank matrix / tensor f...
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11/05/2016
Loss-aware Binarization of Deep Networks
Deep neural network models, though very powerful and highly successful, ...
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06/13/2016
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
The use of convex regularizers allows for easy optimization, though they...
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04/24/2016
Stochastic Variance-Reduced ADMM
The alternating direction method of multipliers (ADMM) is a powerful opt...
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02/25/2016
Fast Nonsmooth Regularized Risk Minimization with Continuation
In regularized risk minimization, the associated optimization problem be...
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12/03/2015