Search Results for author: Haytham M. Fayek

Found 6 papers, 2 papers with code

SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS

no code implementations7 Mar 2024 Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski, Xiaojun Chang

The SWAP-Score is strongly correlated with ground-truth performance across various search spaces and tasks, outperforming 15 existing training-free metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101.

Neural Architecture Search

The Structurally Complex with Additive Parent Causality (SCARY) Dataset

1 code implementation27 Apr 2023 Jarry Chen, Haytham M. Fayek

Our SCARY dataset provides a valuable resource for researchers to explore causal discovery under more realistic scenarios.

Additive models Causal Discovery +1

PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search

no code implementations27 Apr 2022 Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, Xiaojun Chang

This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search.

Neural Architecture Search

Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data

no code implementations29 May 2020 Haytham M. Fayek, Anurag Kumar

Recognizing sounds is a key aspect of computational audio scene analysis and machine perception.

Audio Classification

Temporal Reasoning via Audio Question Answering

1 code implementation21 Nov 2019 Haytham M. Fayek, Justin Johnson

In this paper, we use the task of Audio Question Answering (AQA) to study the temporal reasoning abilities of machine learning models.

Audio Question Answering Question Answering +3

On the Transferability of Representations in Neural Networks Between Datasets and Tasks

no code implementations29 Nov 2018 Haytham M. Fayek, Lawrence Cavedon, Hong Ren Wu

Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers.

Continual Learning Multi-Task Learning

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