research
∙
04/25/2023
Exploring Compositional Visual Generation with Latent Classifier Guidance
Diffusion probabilistic models have achieved enormous success in the fie...
share
research
∙
03/24/2023
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
Conditional image-to-video (cI2V) generation aims to synthesize a new pl...
research
∙
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...
research
∙
01/04/2023
Attribute-Centric Compositional Text-to-Image Generation
Despite the recent impressive breakthroughs in text-to-image generation,...
research
∙
03/29/2022
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Although progress has been made for text-to-image synthesis, previous me...
research
∙
02/24/2022
Learning Transferable Reward for Query Object Localization with Policy Adaptation
We propose a reinforcement learning based approach to query object local...
research
∙
10/17/2021
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
StyleGANs have shown impressive results on data generation and manipulat...
research
∙
08/20/2021
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Conditional Generative Adversarial Networks (cGANs) extend the standard ...
research
∙
03/19/2021
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
This paper considers the problem of spatiotemporal object-centric reason...
research
∙
01/19/2021
Disentangled Recurrent Wasserstein Autoencoder
Learning disentangled representations leads to interpretable models and ...
research
∙
12/08/2020
Molecule Optimization via Fragment-based Generative Models
In drug discovery, molecule optimization is an important step in order t...
research
∙
12/04/2020
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction
T-cell receptors can recognize foreign peptides bound to major histocomp...
research
∙
06/01/2020
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Learning disentangled representations of natural language is essential f...
research
∙
05/23/2020
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
We propose a sequential variational autoencoder to learn disentangled re...
research
∙
09/13/2019
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective
Zero-shot learning (ZSL) aims to recognize instances of unseen classes s...
research
∙
05/13/2019
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly chal...
research
∙
02/27/2019
Disentangled Deep Autoencoding Regularization for Robust Image Classification
In spite of achieving revolutionary successes in machine learning, deep ...
research
∙
11/19/2018
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional...
research
∙
06/21/2018
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Conventional embedding methods directly associate each symbol with a con...
research
∙
05/24/2018
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the composi...
research
∙
11/08/2017
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations
Embedding methods such as word embedding have become pillars for many ap...
research
∙
10/23/2017
Convolutional Neural Knowledge Graph Learning
Previous models for learning entity and relationship embeddings of knowl...
research
∙
10/01/2017
Video Generation From Text
Generating videos from text has proven to be a significant challenge for...
research
∙
09/25/2017
Adaptive Convolutional Filter Generation for Natural Language Understanding
Convolutional neural networks (CNNs) have recently emerged as a popular ...
research
∙
02/21/2017
Exemplar-Centered Supervised Shallow Parametric Data Embedding
Metric learning methods for dimensionality reduction in combination with...
research
∙
12/22/2016
A Context-aware Attention Network for Interactive Question Answering
Neural network based sequence-to-sequence models in an encoder-decoder f...
research
∙
11/23/2016
Adaptive Feature Abstraction for Translating Video to Text
Previous models for video captioning often use the output from a specifi...
research
∙
08/16/2016
A Shallow High-Order Parametric Approach to Data Visualization and Compression
Explicit high-order feature interactions efficiently capture essential s...
research
∙
04/29/2015
A Deep Learning Model for Structured Outputs with High-order Interaction
Many real-world applications are associated with structured data, where ...
research
∙
06/09/2009
