Search Results for author:
Found 13 papers, 7 papers with code
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence.
S2SNet: A Pretrained Neural Network for Superconductivity Discovery
To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures.
$E(2)$-Equivariant Vision Transformer
To address this issue, we design a Group Equivariant Vision Transformer (GE-ViT) via a novel, effective positional encoding operator.
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
There is a growing interest in applying machine learning techniques to healthcare.
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
User Modeling plays an essential role in industry.
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling
However, existing researches have little ability to model universal user representation based on lifelong behavior sequences since user registration.
Interest-oriented Universal User Representation via Contrastive Learning
Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application.
AdaRNN: Adaptive Learning and Forecasting of Time Series
This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data.
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters.
Ranked #1 on
Cross-Lingual ASR
on Common Voice
Learning Invariant Representations across Domains and Tasks
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification.
Learning to Match Distributions for Domain Adaptation
However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias.
Joint Partial Optimal Transport for Open Set Domain Adaptation
However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes.
Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation
Besides, the weighted optimal transport strategy based on SSR is exploited to achieve the precise-pair-wise optimal transport procedure, which reduces negative transfer brought by the samples near decision boundaries in the target domain.
