activity
20162026
most citedLearning to learn by gradient descent by gradient descent

342 citations · 700 across the 14 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2026

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

Francesco Mantegna, Gereon Elvers, Dulhan Jayalath +18

The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to progress from foundational tasks…

cs.LG2025

The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset

Gilad Landau, Miran Özdogan, Gereon Elvers +15

The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising applications is the restoration of communicat…

cs.LG2024★ 1 cited

The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning

Dulhan Jayalath, Gilad Landau, Brendan Shillingford +2

The past few years have seen remarkable progress in the decoding of speech from brain activity, primarily driven by large single-subject datasets. However, due to individual variat…

cs.LG2018★ 104 cited

Sample Efficient Adaptive Text-to-Speech

Yutian Chen, Yannis Assael, Brendan Shillingford +11

We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and ind…

cs.LG2016★ 190 cited

LipNet: End-to-End Sentence-level Lipreading

Yannis M. Assael, Brendan Shillingford, Shimon Whiteson +1

Lipreading is the task of decoding text from the movement of a speaker's mouth. Traditional approaches separated the problem into two stages: designing or learning visual features,…