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Found 10 papers, 7 papers with code
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers
There has long been plenty of theoretical and empirical evidence supporting the success of ensemble learning.
Optimal Transport for Structure Learning Under Missing Data
Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirical shown to be sub-optimal.
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP
Large language models (LLMs), as epitomized by models like ChatGPT, have revolutionized the field of natural language processing (NLP).
Learning Directed Graphical Models with Optimal Transport
Estimating the parameters of a probabilistic directed graphical model from incomplete data remains a long-standing challenge.
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability.
An Additive Instance-Wise Approach to Multi-class Model Interpretation
A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner.
Unsupervised Sentence Simplification via Dependency Parsing
Text simplification is the task of rewriting a text so that it is readable and easily understood.
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI.
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs).
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
In this work, we explore the delayed-RNN, which is a single-layer RNN that has a delay between the input and output.
