Publications

Improving fine-grained understanding in image-text pre-training
Ioana Bica
Anastasija Ili'c
Matthias Bauer
Goker Erdogan
Matko Bovsnjak
Christos Kaplanis
Alexey A. Gritsenko
Matthias Minderer
Charles Blundell
Razvan Pascanu
Jovana Mitrovic
We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations… (see more) from image-text pairs. Given that multiple image patches often correspond to single words, we propose to learn a grouping of image patches for every token in the caption. To achieve this, we use a sparse similarity metric between image patches and language tokens and compute for each token a language-grouped vision embedding as the weighted average of patches. The token and language-grouped vision embeddings are then contrasted through a fine-grained sequence-wise loss that only depends on individual samples and does not require other batch samples as negatives. This enables more detailed information to be learned in a computationally inexpensive manner. SPARC combines this fine-grained loss with a contrastive loss between global image and text embeddings to learn representations that simultaneously encode global and local information. We thoroughly evaluate our proposed method and show improved performance over competing approaches both on image-level tasks relying on coarse-grained information, e.g. classification, as well as region-level tasks relying on fine-grained information, e.g. retrieval, object detection, and segmentation. Moreover, SPARC improves model faithfulness and captioning in foundational vision-language models.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
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 algorithm, …
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng
Florence Regol
Mark J. Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (see more) 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.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Learning to Play Atari in a World of Tokens
Pranav Agarwal
Sheldon Andrews
Samira Ebrahimi Kahou
Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extende… (see more)d context, resulting in more accurate world models. However, for complex reasoning and planning tasks, these methods primarily rely on continuous representations. This complicates modeling of discrete properties of the real world such as disjoint object classes between which interpolation is not plausible. In this work, we introduce discrete abstract representations for transformer-based learning (DART), a sample-efficient method utilizing discrete representations for modeling both the world and learning behavior. We incorporate a transformer-decoder for auto-regressive world modeling and a transformer-encoder for learning behavior by attending to task-relevant cues in the discrete representation of the world model. For handling partial observability, we aggregate information from past time steps as memory tokens. DART outperforms previous state-of-the-art methods that do not use look-ahead search on the Atari 100k sample efficiency benchmark with a median human-normalized score of 0.790 and beats humans in 9 out of 26 games. We release our code at https://pranaval.github.io/DART/.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Learning to Reach Goals via Diffusion
Vineet Jain
Siamak Ravanbakhsh
We present a novel perspective on goal-conditioned reinforcement learning by framing it within the context of denoising diffusion models. An… (see more)alogous to the diffusion process, where Gaussian noise is used to create random trajectories that walk away from the data manifold, we construct trajectories that move away from potential goal states. We then learn a goal-conditioned policy to reverse these deviations, analogous to the score function. This approach, which we call Merlin, can reach specified goals from arbitrary initial states without learning a separate value function. In contrast to recent works utilizing diffusion models in offline RL, Merlin stands out as the first method to perform diffusion in the state space, requiring only one ``denoising" iteration per environment step. We experimentally validate our approach in various offline goal-reaching tasks, demonstrating substantial performance enhancements compared to state-of-the-art methods while improving computational efficiency over other diffusion-based RL methods by an order of magnitude. Our results suggest that this perspective on diffusion for RL is a simple and scalable approach for sequential decision making.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
Shakeeb Murtaza
Marco Pedersoli
Aydin Sarraf
Eric Granger
Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to … (see more)as tags. State-of-the-art WSVOL methods like Temporal CAM (TCAM) rely on class activation mapping (CAM) and typically require a pre-trained CNN classifier. However, their localization accuracy is affected by their tendency to minimize the mutual information between different instances of a class and exploit temporal information during training for downstream tasks, e.g., detection and tracking. In the absence of bounding box annotation, it is challenging to exploit precise information about objects from temporal cues because the model struggles to locate objects over time. To address these issues, a novel method called transformer based CAM for videos (TrCAM-V), is proposed for WSVOL. It consists of a DeiT backbone with two heads for classification and localization. The classification head is trained using standard classification loss (CL), while the localization head is trained using pseudo-labels that are extracted using a pre-trained CLIP model. From these pseudo-labels, the high and low activation values are considered to be foreground and background regions, respectively. Our TrCAM-V method allows training a localization network by sampling pseudo-pixels on the fly from these regions. Additionally, a conditional random field (CRF) loss is employed to align the object boundaries with the foreground map. During inference, the model can process individual frames for real-time localization applications. Extensive experiments on challenging YouTube-Objects unconstrained video datasets show that our TrCAM-V method achieves new state-of-the-art performance in terms of classification and localization accuracy.
2024-07-07
ArXiv (preprint)
doi.org
arxiv.org
Listenable Maps for Audio Classifiers
Francesco Paissan
Mirco Ravanelli
Cem Subakan
Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation. This … (see more)challenge is particularly evident for audio signals, where conveying interpretations becomes inherently difficult. To address this issue, we introduce Listenable Maps for Audio Classifiers (L-MAC), a posthoc interpretation method that generates faithful and listenable interpretations. L-MAC utilizes a decoder on top of a pretrained classifier to generate binary masks that highlight relevant portions of the input audio. We train the decoder with a loss function that maximizes the confidence of the classifier decision on the masked-in portion of the audio while minimizing the probability of model output for the masked-out portion. Quantitative evaluations on both in-domain and out-of-domain data demonstrate that L-MAC consistently produces more faithful interpretations than several gradient and masking-based methodologies. Furthermore, a user study confirms that, on average, users prefer the interpretations generated by the proposed technique.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Lookbehind-SAM: k Steps Back, 1 Step Forward
Goncalo Mordido
Pranshu Malviya
Aristide Baratin
Sarath Chandar
Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and lo… (see more)ss sharpness as a minimax objective. In this work, we increase the efficiency of the maximization and minimization parts of SAM's objective to achieve a better loss-sharpness trade-off. By taking inspiration from the Lookahead optimizer, which uses multiple descent steps ahead, we propose Lookbehind, which performs multiple ascent steps behind to enhance the maximization step of SAM and find a worst-case perturbation with higher loss. Then, to mitigate the variance in the descent step arising from the gathered gradients across the multiple ascent steps, we employ linear interpolation to refine the minimization step. Lookbehind leads to a myriad of benefits across a variety of tasks. Particularly, we show increased generalization performance, greater robustness against noisy weights, as well as improved learning and less catastrophic forgetting in lifelong learning settings. Our code is available at https://github.com/chandar-lab/Lookbehind-SAM.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Meta- (out-of-context) learning in neural networks
Dmitrii Krasheninnikov
Egor Krasheninnikov
Bruno Mlodozeniec
Tegan Maharaj
David M. Krueger
Brown et al. (2020) famously introduced the phenomenon of in-context learning in large language models (LLMs). We establish the existence of… (see more) a phenomenon we call meta-out-of-context learning (meta-OCL) via carefully designed synthetic experiments with LLMs. Our results suggest that meta-OCL leads LLMs to more readily"internalize"the semantic content of text that is, or appears to be, broadly useful (such as true statements, or text from authoritative sources) and use it in appropriate circumstances. We further demonstrate meta-OCL in a synthetic computer vision setting, and propose two hypotheses for the emergence of meta-OCL: one relying on the way models store knowledge in their parameters, and another suggesting that the implicit gradient alignment bias of gradient-descent-based optimizers may be responsible. Finally, we reflect on what our results might imply about capabilities of future AI systems, and discuss potential risks. Our code can be found at https://github.com/krasheninnikov/internalization.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
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 … (see more)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.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
A Persuasive Approach to Combating Misinformation
Safwan Hossain
Andjela Mladenovic
Yiling Chen
Gauthier Gidel
Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine… (see more) learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting and discuss the broader scope of using information design to combat misinformation.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press