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[NeurIPS 2024] BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
[SafeGenAI @ NeurIPS 2024' Oral] On Calibration of LLM-based Guard Models for Reliable Content Moderation
Code and data for the benchmark "Multimodal Needle in a Haystack (MMNeedle): Benchmarking Long-Context Capability of Multimodal Large Language Models"
Official implementation of Earthformer
Continual Learning of Large Language Models: A Comprehensive Survey
[NeurIPS 2023] A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
[ICML 2023] Taxonomy-Structured Domain Adaptation
[ICLR 2023 (Spotlight)] Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
[NeurIPS 2022] Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation
This is the code for the paper Bayesian Invariant Risk Minmization of CVPR 2022.
[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks."
Rethinking Knowledge Graph Propagation for Zero-Shot Learning, in CVPR 2019
[ICML 2020] Continuously Indexed Domain Adaptation
Officially unofficial re-implementation of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.
STRODE: Stochastic Boundary Ordinary Differential Equation
Code for the paper "Correcting Exposure Bias for Link Recommendation (ICML 2021)"
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
A Comparative Framework for Multimodal Recommender Systems
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
[ICLR 2019] ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees