87 citations · 107 across the 6 of their papers we have counts for
7 papers
Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation
Yanghe Dong, Wanting Huang, Weiran Wang
In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fine-tuning (SFT) and model-gen…
Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
Sanat Ramesh, Vinkle Srivastav, Deepak Alapatt +10
The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fu…
A sampling-based approach for efficient clustering in large datasets
Georgios Exarchakis, Omar Oubari, Gregor Lenz
We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of da…
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh +4
ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary component…
Kymatio: Scattering Transforms in Python
Mathieu Andreux, Tomás Angles, Georgios Exarchakis +15
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software packag…
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
Michael Eickenberg, Georgios Exarchakis, Matthew Hirn +2
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory. Using Gaussian-type orbital functions, we create…
Truncated Variational Sampling for "Black Box" Optimization of Generative Models
Jörg Lücke, Zhenwen Dai, Georgios Exarchakis
We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM approach. The approach distinguishes itself from pr…