Search Results for author:
Found 11 papers, 7 papers with code
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
An emerging solution with recent success in vision and NLP involves finetuning a foundation model on a selection of relevant tasks, before its adaptation to a target task with limited labeled samples.
Fourier Circuits in Neural Networks: Unlocking the Potential of Large Language Models in Mathematical Reasoning and Modular Arithmetic
Our research presents a thorough analytical characterization of the features learned by stylized one-hidden layer neural networks and one-layer Transformers in addressing this task.
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes.
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
This paper bridges the gap by providing an analytical framework to formalize and investigate when and how known classes can help discover novel classes.
Domain Generalization via Nuclear Norm Regularization
In this paper, we propose a simple and effective regularization method based on the nuclear norm of the learned features for domain generalization.
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled data, and then learns simple predictors on top of the representation using small labeled data from the downstream tasks.
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
These results provide theoretical evidence showing that feature learning in neural networks depends strongly on the input structure and leads to the superior performance.
Attentive Walk-Aggregating Graph Neural Networks
Our experiments demonstrate the strong performance of AWARE in graph-level prediction tasks in the standard setting in the domains of molecular property prediction and social networks.
Deep Online Fused Video Stabilization
We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning.
SF-Net: Structured Feature Network for Continuous Sign Language Recognition
The proposed SF-Net extracts features in a structured manner and gradually encodes information at the frame level, the gloss level and the sentence level into the feature representation.
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking
Our Dual Augmented Memory Network (DAWN) is unique in remembering both target and background, and using an improved attention LSTM memory to guide the focus on memorized features.
