64 citations · 194 across the 8 of their papers we have counts for
9 papers
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Tal Ridnik, Dedy Kredo, Itamar Friedman
Code generation problems differ from common natural language problems - they require matching the exact syntax of the target language, identifying happy paths and edge cases, payin…
Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results
Tal Ridnik, Hussam Lawen, Emanuel Ben-Baruch +1
ImageNet serves as the primary dataset for evaluating the quality of computer-vision models. The common practice today is training each architecture with a tailor-made scheme, desi…
ML-Decoder: Scalable and Versatile Classification Head
Tal Ridnik, Gilad Sharir, Avi Ben-Cohen +2
In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of…
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman +4
Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Differen…
ImageNet-21K Pretraining for the Masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy +1
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequ…
Asymmetric Loss For Multi-Label Classification
Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir +4
In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, an…