Publications

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
Daniel D. Johnson
Danny Tarlow
David Duvenaud
Chris J. Maddison
Identifying how much a model …
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Graph Positional and Structural Encoder
Renming Liu
Semih Cantürk
Olivier Lapointe-Gagné
Vincent Létourneau
Guy Wolf
Dominique Beaini
Ladislav Rampášek
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node … (voir plus)ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
Stefan Horoi
Albert Manuel Orozco Camacho
Eugene Belilovsky
Guy Wolf
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Implicit meta-learning may lead language models to trust more reliable sources
Dmitrii Krasheninnikov
Egor Krasheninnikov
Bruno Mlodozeniec
Tegan Maharaj
David Scott Krueger
We demonstrate that large language models (LLMs) may learn indicators of document usefulness and modulate their updates accordingly. We intr… (voir plus)oduce random strings ("tags") as indicators of usefulness in a synthetic fine-tuning dataset. Fine-tuning on this dataset leads to **implicit meta-learning (IML)**: in further fine-tuning, the model updates to make more use of text that is tagged as useful. We perform a thorough empirical investigation of this phenomenon, finding (among other things) that (i) it occurs in both pretrained LLMs and those trained from scratch, as well as on a vision task, and (ii) larger models and smaller batch sizes tend to give more IML. We also use probing to examine how IML changes the way models store knowledge in their parameters. Finally, we reflect on what our results might imply about the capabilities, risks, and controllability of future AI systems.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
Improving Gradient-Guided Nested Sampling for Posterior Inference
Pablo Lemos
Nikolay Malkin
Will Handley
Yoshua Bengio
Yashar Hezaveh
Laurence Perreault-Levasseur
We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (voir plus)programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu
Abhay Puri
Juan A. Rodriguez
Alexandre Drouin
Perouz Taslakian
Valentina Zantedeschi
Alexandre Lacoste
David Vazquez
Nicolas Chapados
Chris Pal
Sai Rajeswar
Issam Hadj Laradji
2024-07-08
ArXiv (prépublication)
doi.org
arxiv.org
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng
Florence Regol
Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (voir plus) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
Jinsoo Yoo
Yunpeng Liu
Frank Wood
Geoff Pleiss
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Listenable Maps for Audio Classifiers
Francesco Paissan
Mirco Ravanelli
Cem Subakan
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Lookbehind-SAM: k steps back, 1 step forward
Goncalo Mordido
Pranshu Malviya
Aristide Baratin
Sarath Chandar
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
Modeling Caption Diversity in Contrastive Vision-Language Pretraining
Samuel Lavoie
Polina Kirichenko
Mark Ibrahim
Mahmoud Assran
Andrew Gordon Wilson
Aaron Courville
Nicolas Ballas
There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its … (voir plus)caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an image. In this work, we introduce Llip, Latent Language Image Pretraining, which models the diversity of captions that could match an image. Llip's vision encoder outputs a set of visual features that are mixed into a final representation by conditioning on information derived from the text. We show that Llip outperforms non-contextualized baselines like CLIP and SigLIP on a variety of tasks even with large-scale encoders. Llip improves zero-shot classification by an average of 2.9\% zero-shot classification benchmarks with a ViT-G/14 encoder. Specifically, Llip attains a zero-shot top-1 accuracy of 83.5\% on ImageNet outperforming a similarly sized CLIP by 1.4\%. We also demonstrate improvement on zero-shot retrieval on MS-COCO by 6.0\%. We provide a comprehensive analysis of the components introduced by the method and demonstrate that Llip leads to richer visual representations.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net