Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks.

Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Liam CollinsChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.

Hamed HassaniDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.

Mahdi SoltanolkotabiMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.

Aryan MokhtariChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.

Sanjay ShakkottaiChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.

Funding

Reliable AI for Medical Image ReconstructionDP2LM014564 · NLM · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Mahdi Soltanolkotabi · 2023 to 2026

$2.4M

NLM NIH HHS DP2 LM014564

6 · The paper itself

Abstract

An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This approach yields strong downstream performance in a variety of contexts, demonstrating that multitask pretraining leads to effective feature learning. Although several recent theoretical studies have shown that shallow NNs learn meaningful features when either (i) they are trained on a

Identifiers

PMID39421278

PMCPMC11486479

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.