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03/19/2023
Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks
Wheat is one of the most significant crop species with an annual worldwi...
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03/05/2023
Effectiveness of Data Augmentation for Prefix Tuning with Limited Data
Recent work has demonstrated that tuning continuous prompts on large, fr...
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03/02/2023
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy
T cells monitor the health status of cells by identifying foreign peptid...
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03/02/2023
Over-training with Mixup May Hurt Generalization
Mixup, which creates synthetic training instances by linearly interpolat...
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09/29/2022
Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
Lane changes are complex driving behaviors and frequently involve safety...
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04/21/2022
SoftEdge: Regularizing Graph Classification with Random Soft Edges
Graph data augmentation plays a vital role in regularizing Graph Neural ...
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10/18/2021
Intrusion-Free Graph Mixup
We present a simple and yet effective interpolation-based regularization...
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06/26/2021
Midpoint Regularization: from High Uncertainty Training to Conservative Classification
Label Smoothing (LS) improves model generalization through penalizing mo...
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06/24/2021
Symmetric Wasserstein Autoencoders
Leveraging the framework of Optimal Transport, we introduce a new family...
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06/08/2021
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Reliably predicting the products of chemical reactions presents a fundam...
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06/08/2021
Self-supervised Graph-level Representation Learning with Local and Global Structure
This paper studies unsupervised/self-supervised whole-graph representati...
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12/02/2020
Regularization via Adaptive Pairwise Label Smoothing
Label Smoothing (LS) is an effective regularizer to improve the generali...
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09/02/2020
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
SkipGram word embedding models with negative sampling, or SGN in short, ...
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03/28/2020
A Graph to Graphs Framework for Retrosynthesis Prediction
A fundamental problem in computational chemistry is to find a set of rea...
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01/12/2020
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
We consider the problem of learning a neural network classifier. Under t...
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10/07/2019
Weighted graphlets and deep neural networks for protein structure classification
As proteins with similar structures often have similar functions, analys...
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09/12/2019
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Distant supervision for relation extraction enables one to effectively a...
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06/17/2019
MixUp as Directional Adversarial Training
In this work, we explain the working mechanism of MixUp in terms of adve...
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05/22/2019
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Mixup, a recent proposed data augmentation method through linearly inter...
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09/07/2018
MixUp as Locally Linear Out-Of-Manifold Regularization
MixUp, a data augmentation approach through mixing random samples, has b...
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07/26/2018
Aggregated Learning: A Vector Quantization Approach to Learning with Neural Networks
We establish an equivalence between information bottleneck (IB) learning...
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07/05/2017
A Deep Network with Visual Text Composition Behavior
While natural languages are compositional, how state-of-the-art neural m...
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04/19/2017
End-to-End Multi-View Networks for Text Classification
We propose a multi-view network for text classification. Our method auto...
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02/21/2017
Exemplar-Centered Supervised Shallow Parametric Data Embedding
Metric learning methods for dimensionality reduction in combination with...
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08/16/2016
A Shallow High-Order Parametric Approach to Data Visualization and Compression
Explicit high-order feature interactions efficiently capture essential s...
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10/30/2015
Generating Text with Deep Reinforcement Learning
We introduce a novel schema for sequence to sequence learning with a Dee...
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04/29/2015
A Deep Learning Model for Structured Outputs with High-order Interaction
Many real-world applications are associated with structured data, where ...
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03/16/2015
