Search Results for author:
Found 21 papers, 7 papers with code
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience
Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER).
UniMem: Towards a Unified View of Long-Context Large Language Models
Distinguished by its four core dimensions-Memory Management, Memory Writing, Memory Reading, and Memory Injection, UniMem empowers researchers to conduct systematic exploration of long-context methods.
Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents
In this study, we develop a modular reinforcement learning model that could generate replay.
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Specifically, we consider the general SFT training data, consisting of a small amount of expert data mixed with a large proportion of sub-optimal data, without any preference labels.
Ranked #80 on
Arithmetic Reasoning
on GSM8K
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
Local Hypergraph-based Nested Named Entity Recognition as Query-based Sequence Labeling
There has been a growing academic interest in the recognition of nested named entities in many domains.
Simulated annealing for optimization of graphs and sequences
The key idea is to integrate powerful neural networks into metaheuristics (e. g., simulated annealing, SA) to restrict the search space in discrete optimization.
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition
Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signal representations across subjects when they received the same emotional stimuli in contrast to different ones.
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level.
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.
TrimNet: learning molecular representation from triplet messages for biomedicine
These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.
Ranked #1 on
Drug Discovery
on MUV
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity
Modeling the effects of mutations on the binding affinity plays a crucial role in protein engineering and drug design.
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
Unsupervised Paraphrasing by Simulated Annealing
Unsupervised paraphrase generation is a promising and important research topic in natural language processing.
JUMPER: Learning When to Make Classification Decisions in Reading
Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning.
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
The model consists of two modules.
Event Identification as a Decision Process with Non-linear Representation of Text
We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents.
Estimation of the volume of the left ventricle from MRI images using deep neural networks
In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images.
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Our theoretical analysis shows that stochastic search could in principle even attain optimal network configurations by emulating one of the most well-known nonlinear optimization methods, simulated annealing.
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture.
