◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

C. D. Santos

31 papers hereh-index 3011.6k citations78 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author8
  • middle author22
  • last author1

Across the 31 of 31 papers where every author was matched, so the position is known.

fields
  • cs.CL23
  • cs.LG4
  • cs.IR2
  • cs.CV1
  • eess.IV1
same name
  • C. D. Santos — 2 papers, h 1
  • C. D. Santos — 1 paper, h 11
  • C. D. Santos — 1 paper, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152023
most citedA Structured Self-attentive Sentence Embedding

1.5k citations · 1.7k across the 19 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics

Payel Das, Tom Sercu, Kahini Wadhawan +12

De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…

cs.LG2019

Sobolev Independence Criterion

Youssef Mroueh, Tom Sercu, Mattia Rigotti +2

We propose the Sobolev Independence Criterion (SIC), an interpretable dependency measure between a high dimensional random variable X and a response variable Y . SIC decomposes to…

cs.LG2019★ 15 cited

Wasserstein Barycenter Model Ensembling

Pierre Dognin, Igor Melnyk, Youssef Mroueh +3

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Was…

cs.LG2017★ 24 cited

Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks

Cicero Nogueira dos Santos, Kahini Wadhawan, Bowen Zhou

We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning. Our DAN approach builds upon generative adversarial networks (GANs) an…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.