1 citations · 2 across the 9 of their papers we have counts for
9 papers
Knowledge Editing in Language Models via Adapted Direct Preference Optimization
Amit Rozner, Barak Battash, Lior Wolf +1
Large Language Models (LLMs) can become outdated over time as they may lack updated world knowledge, leading to factual knowledge errors and gaps. Knowledge Editing (KE) aims to ov…
Obtaining Favorable Layouts for Multiple Object Generation
Barak Battash, Amit Rozner, Lior Wolf +1
Large-scale text-to-image models that can generate high-quality and diverse images based on textual prompts have shown remarkable success. These models aim ultimately to create com…
Efficient Verification-Based Face Identification
Amit Rozner, Barak Battash, Ofir Lindenbaum +1
We study the problem of performing face verification with an efficient neural model . The efficiency of stems from simplifying the face verification problem from an embeddin…
Anomaly Detection with Variance Stabilized Density Estimation
Amit Rozner, Barak Battash, Henry Li +2
We propose a modified density estimation problem that is highly effective for detecting anomalies in tabular data. Our approach assumes that the density function is relatively stab…
Revisiting the Noise Model of Stochastic Gradient Descent
Barak Battash, Ofir Lindenbaum
The stochastic gradient noise (SGN) is a significant factor in the success of stochastic gradient descent (SGD). Following the central limit theorem, SGN was initially modeled as G…
Domain-Generalizable Multiple-Domain Clustering
Amit Rozner, Barak Battash, Lior Wolf +1
This work generalizes the problem of unsupervised domain generalization to the case in which no labeled samples are available (completely unsupervised). We are given unlabeled samp…
Mixing between the Cross Entropy and the Expectation Loss Terms
Barak Battash, Lior Wolf, Tamir Hazan
The cross entropy loss is widely used due to its effectiveness and solid theoretical grounding. However, as training progresses, the loss tends to focus on hard to classify samples…
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain
Barak Battash, Haim Barad, Hanlin Tang +1
Video understanding usually requires expensive computation that prohibits its deployment, yet videos contain significant spatiotemporal redundancy that can be exploited. In particu…
Adaptive and Iteratively Improving Recurrent Lateral Connections
Barak Battash, Lior Wolf
The current leading computer vision models are typically feed forward neural models, in which the output of one computational block is passed to the next one sequentially. This is…