Search Results for author:
Found 18 papers, 10 papers with code
InstructCoder: Instruction Tuning Large Language Models for Code Editing
In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions.
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
Ranked #2 on
Node Property Prediction
on ogbn-arxiv
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
In this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation.
CodeInstruct: Empowering Language Models to Edit Code
In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring.
Self-Evaluation Guided Beam Search for Reasoning
Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness.
Meta Pseudo Labels
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90. 2% on ImageNet, which is 1. 6% better than the existing state-of-the-art.
Self-training with Noisy Student improves ImageNet classification
During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher.
Ranked #16 on
Image Classification
on ImageNet ReaL
(using extra training data)
Unsupervised Data Augmentation for Consistency Training
In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.
Ranked #1 on
Sentiment Analysis
on Amazon Review Full
The Profiling Machine: Active Generalization over Knowledge
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation
Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models.
Ranked #18 on
Machine Translation
on WMT2014 English-French
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning.
Large-scale Cloze Test Dataset Designed by Teachers
Cloze test is widely adopted in language exams to evaluate students' language proficiency.
Large-scale Cloze Test Dataset Created by Teachers
Cloze tests are widely adopted in language exams to evaluate students' language proficiency.
Controllable Invariance through Adversarial Feature Learning
Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning.
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness.
RACE: Large-scale ReAding Comprehension Dataset From Examinations
We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task.
Recurrent Polynomial Network for Dialogue State Tracking
Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses.
