activity
20142026
most citedObservation of resonances consistent with pentaquark states in decays

1.3k citations · 13.3k across the 201 of their papers we have counts for

collaborators

204 papers

cs.CL2026

Scaling Properties of Continuous Diffusion Spoken Language Models

Jason Ramapuram, Eeshan Gunesh Dhekane, Amitis Shidani +6

Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicating significant computational…

cs.LG2025

DiceHuBERT: Distilling HuBERT with a Self-Supervised Learning Objective

Hyung Gun Chi, Zakaria Aldeneh, Tatiana Likhomanenko +5

We introduce DiceHuBERT, a knowledge distillation framework for compressing HuBERT, a widely used self-supervised learning (SSL)-based speech foundation model. Unlike existing dist…

eess.AS2024

Speaker-IPL: Unsupervised Learning of Speaker Characteristics with i-Vector based Pseudo-Labels

Zakaria Aldeneh, Takuya Higuchi, Jee-weon Jung +6

Iterative self-training, or iterative pseudo-labeling (IPL) -- using an improved model from the current iteration to provide pseudo-labels for the next iteration -- has proven to b…

eess.AS2024

Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models

Li-Wei Chen, Takuya Higuchi, He Bai +6

Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use…

eess.AS2024

Towards Automatic Assessment of Self-Supervised Speech Models using Rank

Zakaria Aldeneh, Vimal Thilak, Takuya Higuchi +2

This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessi…

cs.LG2024

Theory, Analysis, and Best Practices for Sigmoid Self-Attention

Jason Ramapuram, Federico Danieli, Eeshan Dhekane +8

Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are t…

cs.CL2024★ 1 cited

Generating Gender Alternatives in Machine Translation

Sarthak Garg, Mozhdeh Gheini, Clara Emmanuel +3

Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term "the nurse") into the gendered form that is most prevalent in the systems' training…

cs.SD2024

Can you Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?

Zakaria Aldeneh, Takuya Higuchi, Jee-weon Jung +6

Self-supervised features are typically used in place of filter-bank features in speaker verification models. However, these models were originally designed to ingest filter-bank fe…

cs.LG2023★ 5 cited

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping

Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman +4

While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to…

eess.AS2023★ 1 cited

Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR

Sheikh Shams Azam, Tatiana Likhomanenko, Martin Pelikan +1

In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivo…

stat.ML2023

How to Scale Your EMA

Dan Busbridge, Jason Ramapuram, Pierre Ablin +4

Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-o…

cs.CV2023★ 7 cited

VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON

Haoping Bai, Shancong Mou, Tatiana Likhomanenko +6

Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often…

cs.LG2023★ 8 cited

Stabilizing Transformer Training by Preventing Attention Entropy Collapse

Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin +5

Training stability is of great importance to Transformers. In this work, we investigate the training dynamics of Transformers by examining the evolution of the attention layers. In…

cs.LG2022★ 8 cited

Position Prediction as an Effective Pretraining Strategy

Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram +7

Transformers have gained increasing popularity in a wide range of applications, including Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of thei…

hep-ex2019★ 28 cited

Search for the lepton-flavour violating decays

LHCb collaboration, R. Aaij, C. Abellán Beteta +895

A search for the lepton-flavour violating decays is performed using a sample of proton-proton collision data, collected with the LHCb experiment at ce…

hep-ex2019★ 75 cited

Amplitude analysis of the decay

LHCb collaboration, R. Aaij, C. Abellán Beteta +868

The results of an amplitude analysis of the charmless three-body decay , in which -violation effects are taken into account, are reported. The anal…

hep-ex2019★ 35 cited

Precision measurement of the , and baryon lifetimes

LHCb collaboration, R. Aaij, C. Abellán Beteta +847

We report measurements of the lifetimes of the , and charm baryons using proton-proton collision data at center-of-mass energies of 7 and 8\tev, correspondin…

hep-ex2019★ 18 cited

Measurement of psi(2S) production cross-sections in proton-proton collisions at 7 and 13 TeV

LHCb collaboration, R. Aaij, C. Abellán Beteta +842

The cross-sections of meson production in proton-proton collisions at are measured with a data sample collected by the LHCb detector correspondin…

hep-ex2019★ 16 cited

Measurement of CP violation in the decay and search for the decay

LHCb collaboration, R. Aaij, C. Abellán Beteta +868

A measurement of the time-dependent CP-violating asymmetry in decays is presented. Using a sample of proton-proton collision data corresponding to an integrate…

hep-ex2019★ 14 cited

Observation of the decay

LHCb collaboration, R. Aaij, C. Abellán Beteta +903

Using proton-proton collision data, collected with the LHCb detector and corresponding to 1.0, 2.0 and 1.9fb of integrated luminosity at the centre-of-mass energies of 7, 8,…

hep-ex2019★ 738 cited

Observation of a narrow pentaquark state, , and of two-peak structure of the

LHCb collaboration, R. Aaij, C. Abellán Beteta +866

A narrow pentaquark state, , decaying to is discovered with a statistical significance of in a data sample of decays which is an or…

hep-ex2019★ 15 cited

Amplitude analysis of the decays and measurement of the branching fraction of the decay

LHCb Collaboration, R. Aaij, C. Abellán Beteta +844

The and decays are studied using proton-proton collision data corresponding to an integrated luminosity of 3…

hep-ex2019★ 52 cited

Search for the lepton-flavour-violating decays and

LHCb collaboration, R. Aaij, C. Abellán Beteta +863

A search for and decays is performed using data corresponding to an integrated luminosity of 3 fb of proton-proton coll…

hep-ex2019★ 17 cited

A search for decays

LHCb Collaboration, R. Aaij, C. Abellán Beteta +848

A search for the baryon through the decay is performed with a data sample corresponding to an integrated luminosit…