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Found 6 papers, 1 papers with code
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks.
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents.
Learning to Censor by Noisy Sampling
The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks.
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning
In this work, we introduce AdaSplit which enables efficiently scaling SL to low resource scenarios by reducing bandwidth consumption and improving performance across heterogeneous clients.
Introducing Self-Attention to Target Attentive Graph Neural Networks
Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions.
Ranked #5 on
Session-Based Recommendations
on yoochoose1/64
Rethinking Neural Networks With Benford's Law
To that end, we first define a metric, MLH (Model Enthalpy), that measures the closeness of a set of numbers to Benford's Law and we show empirically that it is a strong predictor of Validation Accuracy.
