32 citations · 43 across the 20 of their papers we have counts for
12 papers · 1 filter
Adaptive Head Budgeting for Efficient Multi-Head Attention
Bilal Faye, Abdoulaye Mbaye, Hanane Azzag +1
Multi-head attention enables Transformers to capture diverse representations, but all attention heads are typically activated for every input, regardless of task complexity. For co…
Value-Free Policy Optimization via Reward Partitioning
Bilal Faye, Hanane Azzag, Mustapha Lebbah
Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by di…
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection
Kodjo Mawuena Amekoe, Mustapha Lebbah, Gregoire Jaffre +2
Real-world tabular learning production scenarios typically involve evolving data streams, where data arrives continuously and its distribution may change over time. In such a setti…
Unsupervised Adaptive Normalization
Bilal Faye, Hanane Azzag, Mustapha Lebbah +1
Deep neural networks have become a staple in solving intricate problems, proving their mettle in a wide array of applications. However, their training process is often hampered by…
Supervised Batch Normalization
Bilal Faye, Mustapha Lebbah, Hanane Azzag
Batch Normalization (BN), a widely-used technique in neural networks, enhances generalization and expedites training by normalizing each mini-batch to the same mean and variance. H…
Lightweight Cross-Modal Representation Learning
Bilal Faye, Hanane Azzag, Mustapha Lebbah +1
Low-cost cross-modal representation learning is crucial for deriving semantic representations across diverse modalities such as text, audio, images, and video. Traditional approach…
Self-Reinforcement Attention Mechanism For Tabular Learning
Kodjo Mawuena Amekoe, Mohamed Djallel Dilmi, Hanene Azzag +3
Apart from the high accuracy of machine learning models, what interests many researchers in real-life problems (e.g., fraud detection, credit scoring) is to find hidden patterns in…
Transformer-based conditional generative adversarial network for multivariate time series generation
Abdellah Madane, Mohamed-djallel Dilmi, Florent Forest +3
Conditional generation of time-dependent data is a task that has much interest, whether for data augmentation, scenario simulation, completing missing data, or other purposes. Rece…
Improved Multi-objective Data Stream Clustering with Time and Memory Optimization
Mohammed Oualid Attaoui, Hanene Azzag, Mustapha Lebbah +1
The analysis of data streams has received considerable attention over the past few decades due to sensors, social media, etc. It aims to recognize patterns in an unordered, infinit…
Algorithms for an Efficient Tensor Biclustering
Andriantsiory Dina Faneva, Mustapha Lebbah, Hanane Azzag +1
Consider a data set collected by (individuals-features) pairs in different times. It can be represented as a tensor of three dimensions (Individuals, features and times). The tenso…
Nearest Neighbor Median Shift Clustering for Binary Data
Gaël Beck, Tarn Duong, Mustapha Lebbah +1
We describe in this paper the theory and practice behind a new modal clustering method for binary data. Our approach (BinNNMS) is based on the nearest neighbor median shift. The me…
A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering
Gaël Beck, Tarn Duong, Mustapha Lebbah +2
In this paper we target the class of modal clustering methods where clusters are defined in terms of the local modes of the probability density function which generates the data. T…