David Vázquez
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  • Bio
  • Contact
  • Experience
  • Projects
  • Publications
  • Projects
    • AI Tools for Indigenous Languages
    • EnterpriseOps-Gym
    • Apriel Model Family
    • BigDocs
    • WorkArena and BrowserGym
    • SYNTHIA
    • Elektra Autonomous Vehicle
  • News
    • We're hiring researchers and engineers
    • Presenting at ICLR 2026 in Rio de Janeiro
    • NSERC Discovery Grant awarded for Indigenous language AI tools
    • AlignVLM accepted at NeurIPS 2025
    • Appointed Adjunct Professor at Polytechnique Montréal
    • EnterpriseOps-Gym Released
    • StarVector accepted at CVPR 2025
    • BigDocs accepted at ICLR 2025
    • WorkArena accepted at ICML 2024
  • Publications
    • Apriel-1.5-OpenReasoner: RL Post-Training for General-Purpose and Efficient Reasoning
    • StarFlow: Generating Structured Workflow Outputs from Sketch Images
    • VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing
    • WildSVG: Towards Reliable SVG Generation Under Real-Word Conditions
    • AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
    • AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding
    • BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning
    • BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks
    • Distilling Specialized Orders for Visual Generation
    • Grounding Computer Use Agents on Human Demonstrations
    • Intent Discovery using Large Language Models
    • Rendering-Aware Reinforcement Learning for Vector Graphics Generation
    • StarVector: Generating Scalable Vector Graphics Code from Images and Text
    • UI-Vision: A Desktop-Centric GUI Benchmark for Visual Perception and Interaction
    • WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
    • Improved Training Set Selection for Semi-Supervised Learning
    • A Multimodal Class-Incremental Learning Benchmark for Classification Tasks
    • CADet: Fully Self-Supervised Out-of-Distribution Detection with Contrastive Learning
    • GEO-Bench: Toward Foundation Models for Earth Monitoring
    • Group Robust Classification without Any Group Information
    • InsightBench: Evaluating Business Analytics Agents through Multi-Step Insight Generation
    • RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
    • Towards Good Validation Metrics for Generative Models in Offline Model-Based Optimisation
    • WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
    • XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
    • Anomaly Detection using Graph Neural Networks
    • Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images
    • Capture the Flag: Uncovering Data Insights with Large Language Models
    • Constraining Representations Yields Models That Know What They Don't Know
    • Expecting the Unexpected: Towards Broad Out-of-Distribution Detection
    • FigGen: Text to Scientific Figure Generation
    • Flaky Performances When Pretraining on Relational Databases
    • Improving Generalization in Task-Oriented Dialogues with Workflows and Action Plans
    • IntentGPT: Few-Shot Intent Discovery with Large Language Models
    • Knowledge Hypergraph Embedding Meets Relational Algebra
    • Language Decision Transformers with Exponential Tilt for Interactive Text Environments
    • Leveraging Human Preferences to Master Poetry
    • Multilingual Code Retrieval without Paired Data: New Datasets and Benchmarks
    • OC-NMN: Object-Centric Compositional Neural Module Network for Generative Visual Analogical Reasoning
    • OCR-VQGAN: Taming Text-within-Image Generation
    • The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
    • TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
    • 3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
    • A Probabilistic Perspective on Reinforcement Learning via Supervised Learning
    • A Survey of Self-Supervised and Few-Shot Object Detection
    • Constraining Low-Level Representations to Define Effective Confidence Scores
    • Contrastive Self-Supervision Defines General-Purpose Similarity Functions
    • CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
    • Data Augmentation for Intent Classification with Off-the-Shelf Large Language Models
    • Exploring Validation Metrics for Offline Model-Based Optimisation with Diffusion Models
    • Flaky Performances When Pretraining on Relational Databases
    • Implicit Offline Reinforcement Learning via Supervised Learning
    • Multi-Label Iterated Learning for Image Classification with Label Ambiguity
    • OCIM: Object-Centric Compositional Imagination for Visual Abstract Reasoning
    • Overcoming Challenges in Leveraging GANs for Few-Shot Data Augmentation
    • Sequoia: A Software Framework to Unify Continual Learning Research
    • Touch-Based Curiosity for Sparse-Reward Tasks
    • Workflow Discovery from Dialogues in the Low Data Regime
    • 3D Perception with Slanted Stixels on GPU
    • A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images
    • A Weakly Supervised Consistency-Based Learning Method for COVID-19 Segmentation in CT Images
    • Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
    • Decoupling Anomaly Discrimination and Representation Learning: Self-Supervised Learning for Anomaly Detection on Attributed Graph
    • Learning Data Augmentation with Online Bilevel Optimization for Image Classification
    • Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
    • SSR: Semi-Supervised Soft Rasterizer for Single-View 2D to 3D Reconstruction
    • Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
    • Weakly Supervised Underwater Fish Segmentation using Affinity LCFCN
    • Counting Objects in Images Based on Approximate Locations
    • A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
    • A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
    • Affinity LCFCN: Learning to Segment Fish with Weak Supervision
    • Counting Cows: Tracking Illegal Cattle Ranching from High-Resolution Satellite Imagery
    • Generating Virtual Images for Promoting Visual Artificial Intelligence
    • Instance Segmentation with Point Supervision
    • LOOC: Localize Overlapping Objects with Count Supervision
    • Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning
    • Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning
    • Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation
    • Proposal-Based Instance Segmentation with Point Supervision
    • Synbols: Probing Learning Algorithms with Synthetic Datasets
    • Adversarial Learning of General Transformations for Data Augmentation
    • Class-Based Styling: Real-Time Localized Style Transfer with Semantic Segmentation
    • Context-Aware Visual Compatibility Prediction
    • Fourier-CPPNs for Image Synthesis
    • Knowledge Hypergraphs: Prediction Beyond Binary Relations
    • Slanted Stixels: A Way to Represent Steep Streets
    • Where Are the Masks: Instance Segmentation with Image-Level Supervision
    • Data for Training Models, Domain Adaptation
    • Environmental Perception for Intelligent Vehicles
    • Learning to Remove Rain in Traffic Surveillance by using Synthetic Data
    • Where Are the Blobs: Counting by Localization with Point Supervision
    • A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
    • GPU-Accelerated Real-Time Stixel Computation
    • Guest Editorial: Deep Learning in Computer Vision
    • On-Board Detection of Pedestrian Intentions
    • Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA
    • Simulation Tools
    • Slanted Stixels: Representing San Francisco's steepest streets
    • The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
    • Training My Car to See using Virtual Worlds
    • Vision-Based Advanced Driver Assistance Systems
    • Comparison of Two Non-Linear Model-Based Control Strategies for Autonomous Vehicles
    • Embedded Real-Time Stereo Estimation via Semi-Global Matching on the GPU
    • From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
    • GPU-Based Pedestrian Detection for Autonomous Driving
    • Hierarchical Adaptive Structural SVM for Domain Adaptation
    • Node-Adapt, Path-Adapt and Tree-Adapt: Model-Transfer Domain Adaptation for Random Forest
    • On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts
    • Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison
    • PixelVAE: A Latent Variable Model for Natural Images
    • The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
    • 3D-Guided Multiscale Sliding Window for Pedestrian Detection
    • Multiview Random Forest of Local Experts Combining RGB and LIDAR Data for Pedestrian Detection
    • Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
    • Vision-Based Offline-Online Perception Paradigm for Autonomous Driving
    • Cost-Sensitive Structured SVM for Multi-Category Domain Adaptation
    • Domain Adaptation of Deformable Part-Based Models
    • Incremental Domain Adaptation of Deformable Part-Based Models
    • Learning a Part-Based Pedestrian Detector in Virtual World
    • Virtual and Real World Adaptation for Pedestrian Detection
    • Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers
    • Adapting Pedestrian Detection from Synthetic to Far Infrared Images
    • Computer Vision Trends and Challenges
    • Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection
    • Interactive Training of Human Detectors
    • Learning a Multiview Part-Based Model in Virtual World for Pedestrian Detection
    • Multi-Task Bilinear Classifiers for Visual Domain Adaptation
    • Occlusion Handling via Random Subspace Classifiers for Human Detection
    • Random Forests of Local Experts for Pedestrian Detection
    • Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes
    • Improving HOG with Image Segmentation: Application to Human Detection
    • Pedestrian Detection: Exploring Virtual Worlds
    • Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection
    • Color Contribution to Part-Based Person Detection in Different Types of Scenarios
    • Cool World: Domain Adaptation of Virtual and Real Worlds for Human Detection using Active Learning
    • Opponent Colors for Human Detection
    • Virtual Worlds and Active Learning for Human Detection
    • Detecting Small Pedestrians
    • Learning Appearance in Virtual Scenarios for Pedestrian Detection
  • Talks & Panels
  • Teaching
  • Team

Presenting at ICLR 2026 in Rio de Janeiro

2026 · 1 min read

Our team presented multiple papers at ICLR 2026 in Rio de Janeiro, including work on multimodal models, web agents, and enterprise AI benchmarks.

Last updated on Apr 8, 2026
David Vázquez
Authors
David Vázquez
I study how machines learn to act in the world.
← We're hiring researchers and engineers Apr 7, 2026
NSERC Discovery Grant awarded for Indigenous language AI tools Nov 1, 2025 →

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