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09/09/2023
When to Learn What: Model-Adaptive Data Augmentation Curriculum
Data augmentation (DA) is widely used to improve the generalization of n...
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08/30/2023
MerA: Merging Pretrained Adapters For Few-Shot Learning
Adapter tuning, which updates only a few parameters, has become a mainst...
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08/23/2023
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
In the realm of Large Language Models, the balance between instruction d...
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07/17/2023
AlpaGasus: Training A Better Alpaca with Fewer Data
Large language models (LLMs) obtain instruction-following capability thr...
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06/29/2023
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
We propose a novel value approximation method, namely Eigensubspace Regu...
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06/28/2023
Subclass-balancing Contrastive Learning for Long-tailed Recognition
Long-tailed recognition with imbalanced class distribution naturally eme...
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06/24/2023
H_2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
Large Language Models (LLMs), despite their recent impressive accomplish...
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06/19/2023
Taming Small-sample Bias in Low-budget Active Learning
Active learning (AL) aims to minimize the annotation cost by only queryi...
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06/16/2023
Structured Cooperative Learning with Graphical Model Priors
We study how to train personalized models for different tasks on decentr...
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06/05/2023
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
Large language models (LLMs) are instruction followers, but it can be ch...
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06/04/2023
A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models
Large Language Models have become popular for their remarkable capabilit...
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05/29/2023
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
How to train a generalizable meta-policy by continually learning a seque...
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05/25/2023
Condensed Prototype Replay for Class Incremental Learning
Incremental learning (IL) suffers from catastrophic forgetting of old ta...
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05/24/2023
Reinforcement Learning finetuned Vision-Code Transformer for UI-to-Code Generation
Automated HTML/CSS code generation from screenshots is an important yet ...
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05/23/2023
Spatial-temporal Prompt Learning for Federated Weather Forecasting
Federated weather forecasting is a promising collaborative learning fram...
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04/27/2023
Large Language Models are Strong Zero-Shot Retriever
In this work, we propose a simple method that applies a large language m...
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04/26/2023
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Large language models (LLMs) are known for their exceptional performance...
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04/09/2023
Does Continual Learning Equally Forget All Parameters?
Distribution shift (e.g., task or domain shift) in continual learning (C...
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04/06/2023
When do you need Chain-of-Thought Prompting for ChatGPT?
Chain-of-Thought (CoT) prompting can effectively elicit complex multi-st...
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04/05/2023
Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models
Large language models (LLMs) have made fundamental changes in human life...
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03/28/2023
Solving Regularized Exp, Cosh and Sinh Regression Problems
In modern machine learning, attention computation is a fundamental task ...
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03/14/2023
It Takes One to Tango but More Make Trouble? In-Context Training with Different Number of Demonstrations
Large language models (LLMs) are capable to perform complex reasoning by...
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01/27/2023
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
As a few large-scale pre-trained models become the major choices of vari...
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01/22/2023
Federated Recommendation with Additive Personalization
With rising concerns about privacy, developing recommendation systems in...
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01/17/2023
Faster Sinkhorn's Algorithm with Small Treewidth
Computing optimal transport (OT) distances such as the earth mover's dis...
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11/26/2022
Faster Algorithm for Structured John Ellipsoid Computation
Computing John Ellipsoid is a fundamental problem in machine learning an...
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11/02/2022
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Data augmentation is a critical contributing factor to the success of de...
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10/27/2022
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
We present Twin Answer Sentences Attack (TASA), an adversarial attack me...
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09/21/2022
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
Federated Learning (FL) is a machine learning paradigm that allows decen...
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05/22/2022
Phrase-level Textual Adversarial Attack with Label Preservation
Generating high-quality textual adversarial examples is critical for inv...
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05/20/2022
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Federated learning (FL) aims at training a global model on the server si...
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03/24/2022
Token Dropping for Efficient BERT Pretraining
Transformer-based models generally allocate the same amount of computati...
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03/02/2022
Personalized Federated Learning With Structure
Knowledge sharing and model personalization are two key components to im...
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02/13/2022
On the Convergence of Clustered Federated Learning
In a federated learning system, the clients, e.g. mobile devices and org...
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09/06/2021
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Aspect-level sentiment classification (ALSC) aims at identifying the sen...
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05/22/2021
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
The learning rate (LR) schedule is one of the most important hyper-param...
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05/01/2021
FedProto: Federated Prototype Learning over Heterogeneous Devices
The heterogeneity across devices usually hinders the optimization conver...
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02/03/2021
Isometric Propagation Network for Generalized Zero-shot Learning
Zero-shot learning (ZSL) aims to classify images of an unseen class only...
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09/24/2020
Attribute Propagation Network for Graph Zero-shot Learning
The goal of zero-shot learning (ZSL) is to train a model to classify sam...
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06/28/2020
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning ...
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05/03/2020
Multi-Center Federated Learning
Federated learning has received great attention for its capability to tr...
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04/30/2020
Semantic Triple Encoder for Fast Open-Set Link Prediction
We improve both the open-set generalization and efficiency of link predi...
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02/24/2020
Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline
For time series classification task using 1D-CNN, the selection of kerne...
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02/18/2020
Conditional Self-Attention for Query-based Summarization
Self-attention mechanisms have achieved great success on a variety of NL...
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02/12/2020
Collaborative Inference for Efficient Remote Monitoring
While current machine learning models have impressive performance over a...
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11/27/2019
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from nois...
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09/11/2019
Learning to Propagate for Graph Meta-Learning
Meta-learning extracts the common knowledge acquired from learning diffe...
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05/10/2019
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
A variety of machine learning applications expect to achieve rapid learn...
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05/02/2018
Fast Directional Self-Attention Mechanism
In this paper, we propose a self-attention mechanism, dubbed "fast direc...
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04/03/2018
