51 citations · 75 across the 13 of their papers we have counts for
19 papers
VANDAM: Viewing a nucleotide sequence with DNA molecular priors
Jeremy Levy, Ariel Larey, Yury Nahshan +18
Contemporary Genomic Foundation Models (GFMs) rely on a DNA-as-a-string paradigm that employs masked token prediction objectives for pretraining. However, this abstraction does not…
WinoTS: Wavelet-based Self-Distillation for Time Series Models
Noam Major, Kathy Razmadze, Yoli Shavit
Self-supervised pre-training of time series models is currently dominated by next-token prediction and reconstruction objectives. In continuous-valued domains, these paradigms ofte…
Cross-Entropy Guided Routing in Mixture-of-Experts Large Language Models
Yury Nahshan, Nati Daniel, Jacob Goldberger +1
Sparse mixture-of-experts (MoE) large language models scale model capacity by routing each token to a small subset of experts. Their routers are regularized with load balancing ter…
When Does Self-Supervised Learning Transfer to Time-Series Tasks?
Noam Major, Kathy Razmadze, Yoli Shavit
Self-supervised learning (SSL) assumes that solving pretext tasks on unlabeled data yields representations that transfer effectively across downstream applications via linear probi…
KappaPlace: Learning Hyperspherical Uncertainty for Visual Place Recognition via Prototype-Anchored Supervision
Maya Yanko, Yoli Shavit
Visual Place Recognition (VPR) is critical for autonomous navigation, yet state-of-the-art methods lack well-calibrated uncertainty estimation. Standard pipelines cannot reliably s…
JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
Ariel Larey, Elay Dahan, Amit Bleiweiss +15
Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local s…
LaVPR: Benchmarking Language and Vision for Place Recognition
Ofer Idan, Dan Badur, Yosi Keller +1
Visual Place Recognition (VPR) often fails under extreme environmental changes and perceptual aliasing. Beyond these limitations, standard systems cannot perform 'blind' localizati…
MoGU: Mixture-of-Gaussians with Uncertainty-based Gating for Time Series Forecasting
Gilad Aviv, Jacob Goldberger, Yoli Shavit
We introduce Mixture-of-Gaussians with Uncertainty-based Gating (MoGU), a novel Mixture-of-Experts (MoE) framework designed for regression tasks. MoGU replaces standard learned gat…
Relative Pose Regression with Pose Auto-Encoders: Enhancing Accuracy and Data Efficiency for Retail Applications
Yoli Shavit, Yosi Keller
Accurate camera localization is crucial for modern retail environments, enabling enhanced customer experiences, streamlined inventory management, and autonomous operations. While A…
Coarse-to-Fine Multi-Scene Pose Regression with Transformers
Yoli Shavit, Ron Ferens, Yosi Keller
Absolute camera pose regressors estimate the position and orientation of a camera given the captured image alone. Typically, a convolutional backbone with a multi-layer perceptron…
Learning to Localize in Unseen Scenes with Relative Pose Regressors
Ofer Idan, Yoli Shavit, Yosi Keller
Relative pose regressors (RPRs) localize a camera by estimating its relative translation and rotation to a pose-labelled reference. Unlike scene coordinate regression and absolute…
Camera Pose Auto-Encoders for Improving Pose Regression
Yoli Shavit, Yosi Keller
Absolute pose regressor (APR) networks are trained to estimate the pose of the camera given a captured image. They compute latent image representations from which the camera positi…
WT-MVSNet: Window-based Transformers for Multi-view Stereo
Jinli Liao, Yikang Ding, Yoli Shavit +5
Recently, Transformers were shown to enhance the performance of multi-view stereo by enabling long-range feature interaction. In this work, we propose Window-based Transformers (WT…
Adversarial Learning of Hard Positives for Place Recognition
Wenxuan Fang, Kai Zhang, Yoli Shavit +1
Image retrieval methods for place recognition learn global image descriptors that are used for fetching geo-tagged images at inference time. Recent works have suggested employing w…
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching
Yan Shi, Jun-Xiong Cai, Yoli Shavit +3
Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a…
Paying Attention to Activation Maps in Camera Pose Regression
Yoli Shavit, Ron Ferens, Yosi Keller
Camera pose regression methods apply a single forward pass to the query image to estimate the camera pose. As such, they offer a fast and light-weight alternative to traditional lo…
Learning Multi-Scene Absolute Pose Regression with Transformers
Yoli Shavit, Ron Ferens, Yosi Keller
Absolute camera pose regressors estimate the position and orientation of a camera from the captured image alone. Typically, a convolutional backbone with a multi-layer perceptron h…
Do We Really Need Scene-specific Pose Encoders?
Yoli Shavit, Ron Ferens
Visual pose regression models estimate the camera pose from a query image with a single forward pass. Current models learn pose encoding from an image using deep convolutional netw…
Introduction to Camera Pose Estimation with Deep Learning
Yoli Shavit, Ron Ferens
Over the last two decades, deep learning has transformed the field of computer vision. Deep convolutional networks were successfully applied to learn different vision tasks such as…