Search Results for author:
Found 16 papers, 1 papers with code
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
Moreover, this task can be used to improve visual question generation and visual question answering.
Pay More Attention to the Robustness of Prompt for Instruction Data Mining
Instruction tuning has emerged as a paramount method for tailoring the behaviors of LLMs.
Exploring structure diversity in atomic resolution microscopy with graph neural networks
The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs.
AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation
AutoFeedback achieves an accuracy of 100. 00\% on a real-world API dataset and reduces the cost of interaction with GPT-3. 5 Turbo by 23. 44\%, and GPT-4 Turbo by 11. 85\%.
Online Self-Preferring Language Models
To overcome this limitation, we propose Online Self-Preferring (OSP) language models to learn from self-generated response pairs and self-judged preference strengths.
IGOT: Information Gain Optimized Tokenizer on Domain Adaptive Pretraining
In this article, we proposed Information Gain Optimized Tokenizer (IGOT), which analyzes the special token set of downstream tasks, constructs a new subset using heuristic function $\phi$ with the special token and its information gain, to build new domain-specific tokenizer, and continues pretraining on the downstream task data.
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
ORPO generates Optimistic model Rollouts for Pessimistic offline policy Optimization.
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles
Reinforcement learning from human feedback (RLHF) emerges as a promising paradigm for aligning large language models (LLMs).
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning
Our method involves training a self-supervised prediction model, saving snapshots of the model parameters, and using nuclear norm to evaluate the temporal inconsistency between the predictions of different snapshots as intrinsic rewards.
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration
The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.
Nuclear Norm Maximization Based Curiosity-Driven Learning
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
FINT: Field-aware INTeraction Neural Network For CTR Prediction
As a critical component for online advertising and marking, click-through rate (CTR) prediction has draw lots of attentions from both industry and academia field.
Exploring Pre-trained Language Models for Event Extraction and Generation
Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size.
Collaborative Deep Learning Across Multiple Data Centers
Valuable training data is often owned by independent organizations and located in multiple data centers.
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network
In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the classification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal.
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network
Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years.
