248 citations · 710 across the 48 of their papers we have counts for
51 papers
Size Generalization for Diffusion Routing Solvers
Barak Itzchak Davidovitch, Eli Meirom, Ethan Fetaya +1
Routing problems, like the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP), are NP-hard and slow to solve for large instances. Diffusion-based s…
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
Dvir Samuel, Guy Bar-Shalom, Fabrizio Frasca +4
Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mi…
Can Modern Vision Models Understand the Difference Between an Object and a Look-alike?
Itay Cohen, Ethan Fetaya, Amir Rosenfeld
Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One su…
Questioning the Stability of Visual Question Answering
Amir Rosenfeld, Neta Glazer, Ethan Fetaya
Visual Language Models (VLMs) have achieved remarkable progress, yet their reliability under small, meaning-preserving input changes remains poorly understood. We present the first…
Drax: Speech Recognition with Discrete Flow Matching
Aviv Navon, Aviv Shamsian, Neta Glazer +4
Diffusion and flow-based non-autoregressive (NAR) models have shown strong promise in large language modeling, however, their potential for automatic speech recognition (ASR) remai…
Expert Merging in Sparse Mixture of Experts with Nash Bargaining
Dung V. Nguyen, Anh T. Nguyen, Minh H. Nguyen +6
Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert parameters, but often lack a prin…
Beyond Transcription: Mechanistic Interpretability in ASR
Neta Glazer, Yael Segal-Feldman, Hilit Segev +6
Interpretability methods have recently gained significant attention, particularly in the context of large language models, enabling insights into linguistic representations, error…
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Neta Glazer, David Chernin, Idan Achituve +2
Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS system…
Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization
Aviv Shamsian, Eitan Shaar, Aviv Navon +2
Machine unlearning aims to remove the influence of problematic training data after a model has been trained. The primary challenge in machine unlearning is ensuring that the proces…
Adversarial Attacks in Weight-Space Classifiers
Tamir Shor, Ethan Fetaya, Chaim Baskin +1
Implicit Neural Representations (INRs) have been recently garnering increasing interest in various research fields, mainly due to their ability to represent large, complex data in…
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Idan Achituve, Hai Victor Habi, Amir Rosenfeld +3
In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known) degradation operator. Commonly…
Conformal Prediction of Classifiers with Many Classes based on Noisy Labels
Coby Penso, Jacob Goldberger, Ethan Fetaya
Conformal Prediction (CP) controls the prediction uncertainty of classification systems by producing a small prediction set, ensuring a predetermined probability that the true clas…
Multi Task Inverse Reinforcement Learning for Common Sense Reward
Neta Glazer, Aviv Navon, Aviv Shamsian +1
One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function. Any misalignm…
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David W. Zhang +4
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural f…
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Idan Achituve, Idit Diamant, Arnon Netzer +2
As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Running a dedicated model for each task is computationally expe…
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
Equivariant Deep Weight Space Alignment
Aviv Navon, Aviv Shamsian, Ethan Fetaya +3
Permutation symmetries of deep networks make basic operations like model merging and similarity estimation challenging. In many cases, aligning the weights of the networks, i.e., f…
Learning Discrete Weights and Activations Using the Local Reparameterization Trick
Guy Berger, Aviv Navon, Ethan Fetaya
In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference. A commonplace solution to address this cha…
GD-VDM: Generated Depth for better Diffusion-based Video Generation
Ariel Lapid, Idan Achituve, Lior Bracha +1
The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, ther…
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
Yochai Yemini, Aviv Shamsian, Lior Bracha +2
Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce h…
DisCLIP: Open-Vocabulary Referring Expression Generation
Lior Bracha, Eitan Shaar, Aviv Shamsian +2
Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achie…
Guided Deep Kernel Learning
Idan Achituve, Gal Chechik, Ethan Fetaya
Combining Gaussian processes with the expressive power of deep neural networks is commonly done nowadays through deep kernel learning (DKL). Unfortunately, due to the kernel optimi…
Auxiliary Learning as an Asymmetric Bargaining Game
Aviv Shamsian, Aviv Navon, Neta Glazer +3
Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets. However, this approach may…
Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon, Aviv Shamsian, Idan Achituve +3
Designing machine learning architectures for processing neural networks in their raw weight matrix form is a newly introduced research direction. Unfortunately, the unique symmetry…