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Ehud Rivlin

7 accepted papers

2024

Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

NAACL 2024short

Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the widespread practice of pre-translation, i.e., translating non-English inputs to English before inference, leading to complexi…

Cited by 13SourcePDFScholar
2024

Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models

NeurIPS 2024poster

The pursuit of high perceptual quality in image restoration has driven the development of revolutionary generative models, capable of producing results often visually indistinguishable from real data. However, as their perceptual quality continues to improve, these models also exhibit a growing tend…

Cited by 3SourcePDFScholar
2024

On the Semantic Latent Space of Diffusion-Based Text-To-Speech Models

ACL 2024short

The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech. Although they exhibit impressive audio quality, the extent of their semantic capabilities is unknown, and controlling their synthesized spee…

2024

Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM

ICLR 2024poster

We present Spectron, a novel approach to adapting pre-trained large language models (LLMs) to perform spoken question answering (QA) and speech continuation. By endowing the LLM with a pre-trained speech encoder, our model becomes able to take speech inputs and generate speech outputs. The entire sy…

Cited by 40SourcePDFScholar
2021

It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems

NeurIPS 2021poster

In recent years there has been increasing interest in leveraging denoisers for solving general inverse problems. Two leading frameworks are regularization-by-denoising (RED) and plug-and-play priors (PnP) which incorporate explicit likelihood functions with priors induced by denoising algorithms. R…

Cited by 72SourcePDFScholar
2015

Estimating camera pose using Bundle Adjustment and Digital Terrain Model constraints

ICRA 2015poster

Bundle Adjustment is the current state of the art method for solving the simultaneous localization and mapping problem. This problem is important for the localization of robots, and most acute for flying robots that cannot rely on ground odometry. The solution requires additional information to reso…

Cited by 8SourceScholar