Publications

Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages

Rahul Aralikatte

Ziling Cheng

Sumanth Doddapaneni

Jackie Cheung

We present V\=arta, a large-scale multilingual dataset for headline generation in Indic languages. This dataset includes 41.8 million news a… (voir plus)rticles in 14 different Indic languages (and English), which come from a variety of high-quality sources. To the best of our knowledge, this is the largest collection of curated articles for Indic languages currently available. We use the data collected in a series of experiments to answer important questions related to Indic NLP and multilinguality research in general. We show that the dataset is challenging even for state-of-the-art abstractive models and that they perform only slightly better than extractive baselines. Owing to its size, we also show that the dataset can be used to pretrain strong language models that outperform competitive baselines in both NLU and NLG benchmarks.

2023-05-10

ArXiv (prépublication)

Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning

Xutong Zhao

Yangchen Pan

Chenjun Xiao

Sarath Chandar

Janarthanan Rajendran

Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration met… (voir plus)hod that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent’s optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at each environment timestep, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent’s state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.

2023-05-08

auai.org/UAI/2023/Conference (publié)

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

Xutong Zhao

Yangchen Pan

Chenjun Xiao

Sarath Chandar

Janarthanan Rajendran

Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration met… (voir plus)hod that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at \textit{each environment timestep}, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent's state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.

2023-05-08

auai.org/UAI/2023/Conference (publié)

Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning

Mohamed Abderrahmen Abid

Arman Afrasiyabi

Ihsen Hedhli

Jean‐François Lalonde

Christian Gagné

2023-05-08

ArXiv (prépublication)

ConceptFusion: Open-set Multimodal 3D Mapping

Krishna Murthy

Alihusein Kuwajerwala

Qiao Gu

Mohd Omama

Tao Chen

Shuang Li

Alaa Maalouf

Ganesh Subramanian Iyer

Soroush Saryazdi

Nikhil Varma Keetha

Ayush Tewari

Joshua B. Tenenbaum

Celso M de Melo

Madhava Krishna

Liam Paull

Florian Shkurti

Antonio Torralba

Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approac… (voir plus)hes that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training time. Further, these maps can only be queried using class labels, or in recent work, using text prompts. We address both these issues with ConceptFusion, a scene representation that is: (i) fundamentally open-set, enabling reasoning beyond a closed set of concepts (ii) inherently multi-modal, enabling a diverse range of possible queries to the 3D map, from language, to images, to audio, to 3D geometry, all working in concert. ConceptFusion leverages the open-set capabilities of today’s foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio. We demonstrate that pixel-aligned open-set features can be fused into 3D maps via traditional SLAM and multi-view fusion approaches. This enables effective zero-shot spatial reasoning, not needing any additional training or finetuning, and retains long-tailed concepts better than supervised approaches, outperforming them by more than 40% margin on 3D IoU. We extensively evaluate ConceptFusion on a number of real-world datasets, simulated home environments, a real-world tabletop manipulation task, and an autonomous driving platform. We showcase new avenues for blending foundation models with 3D open-set multimodal mapping.

2023-05-06

ICRA.org/2023/Workshop/Pretraining4Robotics (publié)

Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study

Erik L. Nygren

Christian Eichenberger

Emma Frejinger

With the aim to stimulate future research, we describe an exploratory study of a railway rescheduling problem. A widely used approach in pra… (voir plus)ctice and state of the art is to decompose these complex problems by geographical scope. Instead, we propose defining a core problem that restricts a rescheduling problem in response to a disturbance to only trains that need to be rescheduled, hence restricting the scope in both time and space. In this context, the difficulty resides in defining a scoper that can predict a subset of train services that will be affected by a given disturbance. We report preliminary results using the Flatland simulation environment that highlights the potential and challenges of this idea. We provide an extensible playground open-source implementation based on the Flatland railway environment and Answer-Set Programming.

2023-05-05

ArXiv (prépublication)

Single-cell analysis reveals inflammatory interactions driving macular degeneration

Manik Kuchroo

Marcello DiStasio

Eric Song

Eda Calapkulu

Le Zhang

Maryam Ige

Amar H. Sheth

Abdelilah Majdoubi

Madhvi Menon

Alexander Tong

Abhinav Godavarthi

Yu Xing

Scott Gigante

Holly Steach

Jessie Huang

Je-chun Huang

Guillaume Huguet

Janhavi Narain

Kisung You

George Mourgkos … (voir 6 de plus)

Rahul M. Dhodapkar

Matthew Hirn

Bastian Rieck

Guy Wolf

Smita Krishnaswamy

Brian P. Hafler

2023-05-05

Nature Communications (publié)

Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model

Mahsa Taghiakbari

Sina Hamidi Ghalehjegh

Emmanuel Jehanno

Tess Berthier

Lisa Di Jorio

Saber Ghadakzadeh

Alan Barkun

Mark Takla

Mickael Bouin

Eric Deslandres

Simon Bouchard

Sacha Sidani

Yoshua Bengio

Daniel von Renteln

Abstract Background and aims Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm comple… (voir plus)teness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps. Methods We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks. Results After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%). Conclusion This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality.

2023-05-02

Journal of the Canadian Association of Gastroenterology (publié)

Cone-Traced Supersampling for Signed Distance Field Rendering

Andrei Chubarau

Yangyang Zhao

Ruby Rao

Paul Kry

Derek Nowrouzezahrai

While Signed Distance Fields (SDFs) in theory offer infinite level of detail, they are typically rendered using the sphere tracing algorithm… (voir plus) at finite resolutions, which causes the common rasterized image synthesis problem of aliasing. Most existing optimized antialiasing solutions rely on polygon mesh representations; SDF-based geometry can only be directly antialiased with the computationally expensive supersampling or with post-processing filters that often lead to undesirable blurriness and ghosting. In this work, we present cone-traced supersampling (CTSS), an efficient and robust spatial antialiasing solution that naturally complements the sphere tracing algorithm, does not require casting additional rays per pixel or offline pre-filtering, and can be easily implemented in existing real-time SDF renderers. CTSS performs supersampling along the traced ray near surfaces with partial visibility identified by evaluating cone intersections within a pixel's view frustum. We further devise a specialized sampling strategy to minimize the number of shading computations and aggregate the collected samples based on their correlated visibility. Depending on configuration, CTSS incurs roughly 15-30% added computational cost and significantly outperforms conventional supersampling approaches while offering comparative antialiasing and visual image quality for most geometric edges.

2023-05-02

graphicsinterface.org/Graphics_Interface/2023/Conference_SD (publié)

Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP

Anya Belz

Craig Thomson

Ehud Reiter

Gavin Abercrombie

Jose M. Alonso-moral

Mohammad Arvan

Jackie Cheung

Mark Cieliebak

Elizabeth Clark

Kees Van Deemter

Tanvi Dinkar

Ondrej Dusek

Steffen Eger

Qixiang Fang

Albert Gatt

Dimitra Gkatzia

Javier Gonz'alez-Corbelle

Dirk Hovy

Manuela Hurlimann

Takumi Ito … (voir 19 de plus)

John D. Kelleher

Filip Klubicka

Huiyuan Lai

Chris van der Lee

Emiel van Miltenburg

Yiru Li

Saad Mahamood

Margot Mieskes

Malvina Nissim

Natalie Paige Parde

Ondvrej Pl'atek

Verena Teresa Rieser

Pablo Mosteiro Romero

Joel Joel Tetreault

Antonio Toral

Xiao-Yi Wan

Leo Wanner

Lewis Joshua Watson

Diyi Yang

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining wha… (voir plus)t makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP.

2023-05-02

ArXiv (prépublication)

On the incompatibility of accuracy and equal opportunity

Carlos Pinzón

Catuscia Palamidessi

Pablo Piantanida

Frank Valencia

2023-05-02

Machine-mediated learning (publié)