Search Results for author: Hani Itani

Found 7 papers, 3 papers with code

Compressed-Language Models for Understanding Compressed File Formats: a JPEG Exploration

no code implementations27 May 2024 Juan C. Pérez, Alejandro Pardo, Mattia Soldan, Hani Itani, Juan Leon-Alcazar, Bernard Ghanem

These results suggest that CLMs can understand the semantics of compressed data when directly operating on the byte streams of files produced by CFFs.

Evaluation of Test-Time Adaptation Under Computational Time Constraints

1 code implementation10 Apr 2023 Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Alhuwaider, Merey Ramazanova, Juan C. Pérez, Zhipeng Cai, Matthias Müller, Bernard Ghanem

To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.

Test-time Adaptation

CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

2 code implementations NeurIPS 2023 Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem

Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github. com/camel-ai/camel.

Instruction Following Language Modelling +1

SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation

no code implementations29 Dec 2020 Hani Itani, Silvio Giancola, Ali Thabet, Bernard Ghanem

Since it is learnable, this mapping is allowed to be different per layer instead of being applied uniformly throughout the depth of the network.

3D Semantic Segmentation

FFTLasso: Large-Scale LASSO in the Fourier Domain

no code implementations CVPR 2017 Adel Bibi, Hani Itani, Bernard Ghanem

Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e. g. on a GPU).

Dimensionality Reduction Face Recognition +2

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