7 citations · 24 across the 17 of their papers we have counts for
18 papers
A Quantum Approach to Stochastic Optimization in Insurance Underwriting
Mitchell Bordelon, Maurice Garfinkel, Vivek Dixit +7
The presence of stochastic elements in combinatorial optimization problems makes them particularly challenging, as such problems quickly become intractable for classical computers…
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…
Tricks and Plug-ins for Gradient Boosting in Image Classification
Biyi Fang, Truong Vo, Jean Utke +1
Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep archite…
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…
Unsupervised Video Summarization via Iterative Training and Simplified GAN
Hanqing Li, Diego Klabjan, Jean Utke
This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simp…
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
Bo-Ru Lu, Nikita Haduong, Chia-Hsuan Lee +7
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fac…
S-Omninet: Structured Data Enhanced Universal Multimodal Learning Architecture
Ye Xue, Diego Klabjan, Jean Utke
Multimodal multitask learning has attracted an increasing interest in recent years. Singlemodal models have been advancing rapidly and have achieved astonishing results on various…
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…
Unified recurrent neural network for many feature types
Alexander Stec, Diego Klabjan, Jean Utke
There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. asynchronous time series, provide a richer vari…
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…
Nested multi-instance classification
Alexander Stec, Diego Klabjan, Jean Utke
There are classification tasks that take as inputs groups of images rather than single images. In order to address such situations, we introduce a nested multi-instance deep networ…