no code implementations • 3 Oct 2024 • Yotam Intrator, Ori Kelner, Regev Cohen, Roman Goldenberg, Ehud Rivlin, Daniel Freedman
Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees.
no code implementations • 21 Jul 2024 • Idan Kligvasser, Regev Cohen, George Leifman, Ehud Rivlin, Michael Elad
Furthermore, during inference, we leverage the transformer architecture to modify the diffusion process, generating a batch of non-uniform sequences anchored to a common frame, ensuring consistency regardless of temporal distance.
no code implementations • 26 Jun 2024 • Matan Halfon, Eyal Rozenberg, Ehud Rivlin, Daniel Freedman
Machine learning approaches to Structure-Based Drug Design (SBDD) have proven quite fertile over the last few years.
no code implementations • 26 May 2024 • Regev Cohen, Idan Kligvasser, Ehud Rivlin, Daniel Freedman
However, as their perceptual quality continues to improve, these models also exhibit a growing tendency to generate hallucinations - realistic-looking details that do not exist in the ground truth images.
no code implementations • 19 May 2024 • Omer Belhasin, Idan Kligvasser, George Leifman, Regev Cohen, Erin Rainaldi, Li-Fang Cheng, Nishant Verma, Paul Varghese, Ehud Rivlin, Michael Elad
Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades.
no code implementations • 29 Apr 2024 • Khaled Saab, Tao Tu, Wei-Hung Weng, Ryutaro Tanno, David Stutz, Ellery Wulczyn, Fan Zhang, Tim Strother, Chunjong Park, Elahe Vedadi, Juanma Zambrano Chaves, Szu-Yeu Hu, Mike Schaekermann, Aishwarya Kamath, Yong Cheng, David G. T. Barrett, Cathy Cheung, Basil Mustafa, Anil Palepu, Daniel McDuff, Le Hou, Tomer Golany, Luyang Liu, Jean-Baptiste Alayrac, Neil Houlsby, Nenad Tomasev, Jan Freyberg, Charles Lau, Jonas Kemp, Jeremy Lai, Shekoofeh Azizi, Kimberly Kanada, SiWai Man, Kavita Kulkarni, Ruoxi Sun, Siamak Shakeri, Luheng He, Ben Caine, Albert Webson, Natasha Latysheva, Melvin Johnson, Philip Mansfield, Jian Lu, Ehud Rivlin, Jesper Anderson, Bradley Green, Renee Wong, Jonathan Krause, Jonathon Shlens, Ewa Dominowska, S. M. Ali Eslami, Katherine Chou, Claire Cui, Oriol Vinyals, Koray Kavukcuoglu, James Manyika, Jeff Dean, Demis Hassabis, Yossi Matias, Dale Webster, Joelle Barral, Greg Corrado, Christopher Semturs, S. Sara Mahdavi, Juraj Gottweis, Alan Karthikesalingam, Vivek Natarajan
We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin.
Ranked #1 on
Question Answering
on MedQA
(using extra training data)
no code implementations • 14 Mar 2024 • Joel Shor, Carson McNeil, Yotam Intrator, Joseph R Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg
We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours).
no code implementations • 4 Mar 2024 • Yotam Intrator, Matan Halfon, Roman Goldenberg, Reut Tsarfaty, Matan Eyal, Ehud Rivlin, Yossi Matias, Natalia Aizenberg
Large language models hold significant promise in multilingual applications.
no code implementations • 19 Feb 2024 • Miri Varshavsky-Hassid, Roy Hirsch, Regev Cohen, Tomer Golany, Daniel Freedman, Ehud Rivlin
The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech.
no code implementations • 11 Dec 2023 • Joel Shor, Hiro-o Yamano, Daisuke Tsurumaru, Yotami Intrator, Hiroki Kayama, Joe Ledsam, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Eiji Oki, Roman Goldenberg, Ehud Rivlin, Ichiro Takemasa
$\textbf{Conclusion}$: Differences that prevent CADe detectors from performing well in non-medical settings do not degrade the performance of our AI CADe polyp detector when applied to data from a new country.
no code implementations • 26 Oct 2023 • Roy Hirsch, Regev Cohen, Mathilde Caron, Tomer Golany, Daniel Freedman, Ehud Rivlin
A key element of computer-assisted surgery systems is phase recognition of surgical videos.
1 code implementation • 23 Aug 2023 • Roy Hirsch, Mathilde Caron, Regev Cohen, Amir Livne, Ron Shapiro, Tomer Golany, Roman Goldenberg, Daniel Freedman, Ehud Rivlin
To fully exploit the power of SSL, we create sizable unlabeled endoscopic video datasets for training MSNs.
Ranked #2 on
Surgical phase recognition
on Cholec80
no code implementations • 14 Jun 2023 • Yotam Intrator, Natalie Aizenberg, Amir Livne, Ehud Rivlin, Roman Goldenberg
Computer-aided polyp detection (CADe) is becoming a standard, integral part of any modern colonoscopy system.
no code implementations • 12 Jun 2023 • Ori Kelner, Or Weinstein, Ehud Rivlin, Roman Goldenberg
Following the successful debut of polyp detection and characterization, more advanced automation tools are being developed for colonoscopy.
no code implementations • 24 May 2023 • Eliya Nachmani, Alon Levkovitch, Roy Hirsch, Julian Salazar, Chulayuth Asawaroengchai, Soroosh Mariooryad, Ehud Rivlin, RJ Skerry-Ryan, Michelle Tadmor Ramanovich
Key to our approach is a training objective that jointly supervises speech recognition, text continuation, and speech synthesis using only paired speech-text pairs, enabling a `cross-modal' chain-of-thought within a single decoding pass.
1 code implementation • 17 May 2023 • Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad
Uncertainty quantification for inverse problems in imaging has drawn much attention lately.
no code implementations • 17 May 2023 • Idan Kligvasser, George Leifman, Roman Goldenberg, Ehud Rivlin, Michael Elad
By integrating the local metric over the withdrawal phase, we build a global, offline quality metric, which is shown to be highly correlated to the standard Polyp Per Colonoscopy (PPC) quality metric.
no code implementations • 10 Mar 2023 • Joel Shor, Ruyue Agnes Bi, Subhashini Venugopalan, Steven Ibara, Roman Goldenberg, Ehud Rivlin
We demonstrate that this metric more closely aligns with clinician preferences on medical sentences as compared to other metrics (WER, BLUE, METEOR, etc), sometimes by wide margins.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
no code implementations • 8 Feb 2023 • Guy Bar-Shalom, George Leifman, Michael Elad, Ehud Rivlin
This paper introduces LRProp -- a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category.
no code implementations • 16 Oct 2022 • Ori Kelner, Or Weinstein, Ehud Rivlin, Roman Goldenberg
We propose a two-stage unsupervised approach for parsing videos into phases.
no code implementations • 13 Jun 2022 • Tom Avrech, Evgenii Zheltonozhskii, Chaim Baskin, Ehud Rivlin
In this work, we present a novel method for real-time environment exploration, whose only requirements are a visually similar dataset for pre-training, enough lighting in the scene, and an on-board forward-looking RGB camera for environmental sensing.
no code implementations • NeurIPS 2021 • Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin
In this work, we introduce image denoisers derived as the gradients of smooth scalar-valued deep neural networks, acting as potentials.
no code implementations • 23 Jan 2020 • Daniel Freedman, Yochai Blau, Liran Katzir, Amit Aides, Ilan Shimshoni, Danny Veikherman, Tomer Golany, Ariel Gordon, Greg Corrado, Yossi Matias, Ehud Rivlin
Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.
no code implementations • 17 Jun 2015 • Daniel J. Mankowitz, Ehud Rivlin
CFORB has also been run in an indoor environment and achieved an average translational error of $3. 70 \%$.