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Found 16 papers, 2 papers with code
A Chance-Constrained Generative Framework for Sequence Optimization
Deep generative modeling has achieved many successes for continuous data generation, such as producing realistic images and controlling their properties (e. g., styles).
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution.
Vector-Quantized Prompt Learning for Paraphrase Generation
Therefore, we present vector-quantized prompts as the cues to control the generation of pre-trained models.
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time.
Weakly Supervised Reasoning by Neuro-Symbolic Approaches
Deep learning has largely improved the performance of various natural language processing (NLP) tasks.
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically.
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning.
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.
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.
Unsupervised Paraphrasing by Simulated Annealing
Unsupervised paraphrase generation is a promising and important research topic in natural language processing.
Shift-based Primitives for Efficient Convolutional Neural Networks
These three primitives (channel shift, address shift, shortcut shift) can reduce the inference time on GPU while maintains the prediction accuracy.
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.
Deep-learning Based Modeling of Fault Detachment Stability for Power Grid
The so-called "fail-delay cut-off" refers to the occurrence of N-1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault.
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains.
