Adriana Romero Soriano

Jakob Verbeek

Michal Drozdzal

2023-10-05

TMLR (accepted)

Controllable Image Generation via Collage Representations

Arantxa Casanova

Marlene Careil

Chris Pal

Jakob Verbeek

Michal Drozdzal

2023-04-26

ArXiv (preprint)

Instance-Conditioned GAN Data Augmentation for Representation Learning

Pietro Astolfi

Arantxa Casanova

Jakob Verbeek

Pascal Vincent

Michal Drozdzal

2023-03-16

ArXiv (preprint)

Learning to Substitute Ingredients in Recipes

Bahare Fatemi

Quentin Duval

Rohit Girdhar

Michal Drozdzal

Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid pote… (see more)ntial allergens, and ease culinary exploration in everyone's kitchen. To address ingredient substitution, we build a benchmark, composed of a dataset of substitution pairs with standardized splits, evaluation metrics, and baselines. We further introduce Graph-based Ingredient Substitution Module (GISMo), a novel model that leverages the context of a recipe as well as generic ingredient relational information encoded within a graph to rank plausible substitutions. We show through comprehensive experimental validation that GISMo surpasses the best performing baseline by a large margin in terms of mean reciprocal rank. Finally, we highlight the benefits of GISMo by integrating it in an improved image-to-recipe generation pipeline, enabling recipe personalization through user intervention. Quantitative and qualitative results show the efficacy of our proposed system, paving the road towards truly personalized cooking and tasting experiences.

2023-02-15

ArXiv (preprint)

On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects

Sumana Basu

M. Legault

Doina Precup

Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify … (see more)two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged affect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favourable qualitative behavior in our policy analysis.

2023-01-02

ArXiv (preprint)

Graph Inductive Biases in Transformers without Message Passing

Liheng Ma

Chen Lin

Derek Lim

Puneet K. Dokania

Mark Coates

Philip Torr

Ser-Nam Lim

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (see more)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

2023-01-01

ICML (published)

Graph Inductive Biases in Transformers without Message Passing

Liheng Ma

Chen Lin

Derek Lim

Puneet K. Dokania

Mark Coates

Philip Torr

Ser-Nam Lim

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (see more)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) — a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive — it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

2023-01-01

ICML (published)

Revisiting Hotels-50K and Hotel-ID

Aarash Feizi

Arantxa Casanova

Reihaneh Rabbany

In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions prov… (see more)ide evaluation setups with different levels of difﬁculty to better align with the intended real-world application, i.e. countering human trafﬁcking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.

2022-07-20

ArXiv (preprint)

Harvesting Mature Relation Extraction Models from Limited Seed Knowledge: A Self-Development Framework for DS Rule Expansion

Raphael Hoffmann

Congle Zhang

Xiao Ling

Yankai Lin

Shiqi Shen

Zhiyuan Liu

Huanbo Luan

Christopher D Manning

M. Surdeanu

John Bauer

Pietro Lio’

Yoshua Bengio

Xuanhui Wang

Cheng Li

Nadav Golbandi

Bendersky Marc

Najork. 2018

The

Wentao Wu … (see 2 more)

Hongsong Li

Haixun Wang

Distantly-supervised relation extraction 001 (DSRE) is an effective method to scale relation 002 extraction (RE) to large unlabeled corpora … (see more)003 with the utilization of knowledge bases (KBs), 004 but suffers from the scale of KBs and the 005 introduced noise. 006 To alleviate the above two problems, we 007 propose a novel framework called S elf-008 devel O pment r U le ex P ansion ( SOUP ), which 009 starts from limited amount of labeled data 010 and continuously produces low-noise labels on 011 large-scaled unlabeled data by a growing learn-012 able logical rules set. 013 Specifically, SOUP achieves a mutual enhance-014 ment of RE model and logical rules set, first 015 a RE model is trained on the labeled data to 016 summarize the knowledge, then the knowledge 017 is utilized to explore candidate rules from unla-018 beled data, finally high-quality candidates are 019 selected in a graph-based ranking manner to ex-020 tend the logical rules set and new rule-labeled 021 data are provided for better RE model training. 022 Experiments on wiki20 dataset demonstrate 023 that, with limited seed knowledge from small-024 scaled manually labeled data, SOUP achieves 025 significant improvement compared to baselines 026 by producing continuous growth of both logical 027 rules and the RE model, and that labeling noise 028 of SOUP is much less than DS. Furthermore, 029 RE model enhanced by SOUP with 1.6k logical 030 rules learned from prior knowledge could pro-031 duce an equivalent performance to the model 032 trained on data labeled in DS manner by 72k 033 relational facts of KBs. 034

TaHiD: Tackling Data Hiding in Fake News Detection with News Propagation Networks

Adrien Benamira

Benjamin Devillers

Etienne Lesot

Ayush K. Ray

Manal Saadi

Fragkiskos D 587

Steven Bird

Ewan Klein

Edward Loper

Nat-593

Carlos Castillo

Marcelo Mendoza

Barbara Poblete

Daryna Dementieva

Alexander Panchenko

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Ashish Vaswani

Noam M. Shazeer … (see 8 more)

Niki Parmar

Pietro Lio’

Yoshua Bengio

Yaqing Wang

Fenglong Ma

Zhiwei Jin

Ye Yuan

Fake news with detrimental societal effects has 001 attracted extensive attention and research. De-002 spite early success, the state-of-the… (see more)-art meth-003 ods fall short of considering the propagation 004 of news. News propagates at different times 005 through different mediums, including users, 006 comments, and sources, which form the news 007 propagation network. Moreover, the serious 008 problem of data hiding arises, which means 009 that fake news publishers disguise fake news 010 as real to confuse users by deleting comments 011 that refute the rumor or deleting the news itself 012 when it has been spread widely. Existing meth-013 ods do not consider the propagation of news 014 and fail to identify what matters in the process, 015 which leads to fake news hiding in the prop-016 agation network and escaping from detection. 017 Inspired by the propagation of news, we pro-018 pose a novel fake news detection framework 019 named TaHiD, which models the propagation 020 as a heterogeneous dynamic graph and contains 021 the propagation attention module to measure 022 the influence of different propagation. Exper-023 iments demonstrate that TaHiD extracts use-024 ful information from the news propagation net-025 work and outperforms state-of-the-art methods 026 on several benchmark datasets for fake news 027 detection. Additional studies also show that 028 TaHiD is capable of identifying fake news in 029 the case of data hiding. 030

Graph Attention Networks with Positional Embeddings

Liheng Ma

Reihaneh Rabbany

2021-05-09

Advances in Knowledge Discovery and Data Mining (published)

A Simple and Effective Model for Multi-Hop Question Generation

Jimmy Lei Ba

Jamie Ryan Kiros

Geoffrey E Hin-602

Peter W. Battaglia

Jessica Blake

Chandler Hamrick

Vic-613 tor Bapst

Alvaro Sanchez

Vinicius Zambaldi

M. Malinowski

Andrea Tacchetti

David Raposo

Tom B. Brown

Benjamin Mann

Nick Ryder

Melanie Subbiah

Jared Kaplan

Prafulla Dhariwal

Arvind Neelakantan

Pranav Shyam … (see 72 more)

Girish Sastry

Koustuv Sinha

Shagun Sodhani

Jin Dong

Joelle Pineau

William L. Hamilton

Clutrr

Nitish Srivastava

Geoffrey Hinton

Alex Krizhevsky

Ilya Sutskever

Ruslan Salakhutdinov. 2014

Gabriel Stanovsky

Julian Michael

Luke Zettlemoyer

Dan Su

Yan Xu

Wenliang Dai

Ziwei Ji

Tiezheng Yu

Minghao Tu

Kevin Huang

Guangtao Wang

Jing Huang

Ashish Vaswani

Noam M. Shazeer

Niki Parmar

Jakob Uszkoreit

Llion Jones

Aidan N. Gomez

Łukasz Kaiser

Illia Polosukhin. 2017

Attention

Petar Veliˇckovi´c

Guillem Cucurull

Arantxa Casanova