7 citations · 24 across the 17 of their papers we have counts for
11 papers · 1 filter
Tricks and Plug-ins for Gradient Boosting with Transformers
Biyi Fang, Truong Vo, Jean Utke +1
Transformer architectures dominate modern NLP but often demand heavy computational resources and intricate hyperparameter tuning. To mitigate these challenges, we propose a novel f…
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
Yintai Ma, Diego Klabjan, Jean Utke
The development of sophisticated models for video-to-video synthesis has been facilitated by recent advances in deep reinforcement learning and generative adversarial networks (GAN…
IIFE: Interaction Information Based Automated Feature Engineering
Tom Overman, Diego Klabjan, Jean Utke
Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance. While traditional fe…
Early Classifying Multimodal Sequences
Alexander Cao, Jean Utke, Diego Klabjan
Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classi…
A Policy for Early Sequence Classification
Alexander Cao, Jean Utke, Diego Klabjan
Sequences are often not received in their entirety at once, but instead, received incrementally over time, element by element. Early predictions yielding a higher benefit, one aims…
Gradient-Boosted Based Structured and Unstructured Learning
Andrea Treviño Gavito, Diego Klabjan, Jean Utke
We propose two frameworks to deal with problem settings in which both structured and unstructured data are available. Structured data problems are best solved by traditional machin…
Multi-Layer Attention-Based Explainability via Transformers for Tabular Data
Andrea Treviño Gavito, Diego Klabjan, Jean Utke
We propose a graph-oriented attention-based explainability method for tabular data. Tasks involving tabular data have been solved mostly using traditional tree-based machine learni…
Tricks and Plugins to GBM on Images and Sequences
Biyi Fang, Jean Utke, Diego Klabjan
Convolutional neural networks (CNNs) and transformers, which are composed of multiple processing layers and blocks to learn the representations of data with multiple abstract level…
Classification Models for Partially Ordered Sequences
Stephanie Ger, Diego Klabjan, Jean Utke
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 i…
Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples
Jaehoon Koo, Diego Klabjan, Jean Utke
Most recent machine learning research focuses on developing new classifiers for the sake of improving classification accuracy. With many well-performing state-of-the-art classifier…
Combined convolutional and recurrent neural networks for hierarchical classification of images
Jaehoon Koo, Diego Klabjan, Jean Utke
Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a featur…