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Found 16 papers, 2 papers with code
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
The development of sophisticated models for video-to-video synthesis has been facilitated by recent advances in deep reinforcement learning and generative adversarial networks (GANs).
IIFE: Interaction Information Based Automated Feature Engineering
Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance.
Unsupervised Video Summarization via Iterative Training and Simplified GAN
This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simple loss function, and separating training of different parts of the model.
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons.
S-Omninet: Structured Data Enhanced Universal Multimodal Learning Architecture
In this work, we extend and improve Omninet, an architecture that is capable of handling multiple modalities and tasks at a time, by introducing cross-cache attention, integrating patch embeddings for vision inputs, and supporting structured data.
Early Classifying Multimodal Sequences
Often pieces of information are received sequentially over time.
A Policy for Early Sequence Classification
Sequences are often not received in their entirety at once, but instead, received incrementally over time, element by element.
Gradient-Boosted Based Structured and Unstructured Learning
Our proposed frameworks allow joint learning on both kinds of data by integrating the paradigms of boosting models and deep neural networks.
Multi-Layer Attention-Based Explainability via Transformers for Tabular Data
We propose a graph-oriented attention-based explainability method for tabular data.
Tricks and Plugins to GBM on Images and Sequences
Convolutional neural networks (CNNs) and transformers, which are composed of multiple processing layers and blocks to learn the representations of data with multiple abstract levels, are the most successful machine learning models in recent years.
Classification Models for Partially Ordered Sequences
Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units (GRUs) and transformers have been developed to classify time series data with the assumption that events in a sequence are ordered.
Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples
In this study, we propose a new framework to solve inverse classification that maximizes the number of perturbed samples subject to a per-feature-budget limits and favorable classification classes of the perturbed samples.
Unified recurrent network for many feature types
We also include two types of static (whole sequence level) features, one related to time and one not, which are combined with the encoder output.
Combined convolutional and recurrent neural networks for hierarchical classification of images
Deep learning models based on CNNs are predominantly used in image classification tasks.
Unified recurrent neural network for many feature types
We also include two types of static (whole sequence level) features, one related to time and one not, which are combined with the encoder output.
Nested multi-instance classification
We also introduce a method to replace instances that are missing which successfully creates neutral input instances and consistently outperforms standard fill-in methods in real world use cases.
