Search Results for author:
Found 15 papers, 6 papers with code
DetGPT: Detect What You Need via Reasoning
Overall, our proposed paradigm and DetGPT demonstrate the potential for more sophisticated and intuitive interactions between humans and machines.
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) as a means of addressing this problem, wherein generative models are fine-tuned using RL algorithms guided by a human-feedback-informed reward model.
Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium
We consider the general nonconvex nonconcave minimax problem over continuous variables.
Vocabulary-informed Zero-shot and Open-set Learning
Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels.
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
With the popularity of Stein variational gradient descent (SVGD), the focus of particle-based VI algorithms has been on the properties of functions in Reproducing Kernel Hilbert Space (RKHS) to approximate the gradient flow.
Normalizing Flow with Variational Latent Representation
The resulting method is significantly more powerful than the standard normalization flow approach for generating data distributions with multiple modes.
How Powerful is Implicit Denoising in Graph Neural Networks
However, the analysis of implicit denoising effect in graph neural networks remains open.
Bayesian Invariant Risk Minimization
Generalization under distributional shift is an open challenge for machine learning.
Local Augmentation for Graph Neural Networks
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
Mathematical Models of Overparameterized Neural Networks
Deep learning has received considerable empirical successes in recent years.
Weakly Supervised Disentangled Generative Causal Representation Learning
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.
Higher-order Weighted Graph Convolutional Networks
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations
Recently, over-parameterized neural networks have been extensively analyzed in the literature.
Learning the Compositional Spaces for Generalized Zero-shot Learning
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time.
Vocabulary-informed Extreme Value Learning
To solve this problem, we propose the Extreme Value Learning (EVL) formulation to learn the mapping from visual feature to semantic space.
