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Publications
Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting
Spurious correlations are a major source of errors for machine learning models, in particular when aiming for group-level fairness. It has b… (voir plus)een recently shown that a powerful approach to combat spurious correlations is to re-train the last layer on a balanced validation dataset, isolating robust features for the predictor. However, key attributes can sometimes be discarded by neural networks towards the last layer. In this work, we thus consider retraining a classifier on a set of features derived from all layers. We utilize a recently proposed feature selection strategy to select unbiased features from all the layers. We observe this approach gives significant improvements in worst-group accuracy on several standard benchmarks.
Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained … (voir plus)on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFY’s pre-training codebase, data, and model checkpoints.
We introduce a self supervised framework for learning representations in the context of dictionary learning. We cast the problem as a kernel… (voir plus) matching task between the input and the representation space, with constraints on the latent kernel. By adjusting these constraints, we demonstrate how the framework can adapt to different learning objectives. We then formulate a novel Alternate Direction Method of Multipli-ers (ADMM) based algorithm to solve the optimization problem and connect the dynamics to classical alternate minimization techniques. This approach offers a unique way of learning representations with kernel constraints, that enable us implicitly learn a generative map for the data from the learned representations which can have broad applications in representation learning tasks both in machine learning and neuro-science.
2024-09-22
International Workshop on Machine Learning for Signal Processing (publié)
We introduce a self supervised framework for learning representations in the context of dictionary learning. We cast the problem as a kernel… (voir plus) matching task between the input and the representation space, with constraints on the latent kernel. By adjusting these constraints, we demonstrate how the framework can adapt to different learning objectives. We then formulate a novel Alternate Direction Method of Multipli-ers (ADMM) based algorithm to solve the optimization problem and connect the dynamics to classical alternate minimization techniques. This approach offers a unique way of learning representations with kernel constraints, that enable us implicitly learn a generative map for the data from the learned representations which can have broad applications in representation learning tasks both in machine learning and neuro-science.
2024-09-22
International Workshop on Machine Learning for Signal Processing (publié)
In audio and speech processing, tasks usually focus on either the audio or speech modality, even when both sounds and human speech are prese… (voir plus)nt in the same audio clip. Recent Auditory Large Language Models (ALLMs) have made it possible to process audio and speech simultaneously within a single model, leading to further considerations of joint audio-speech tasks. In this paper, we establish a novel benchmark to investigate how well ALLMs can perform joint audio-speech processing. Specifically, we introduce Joint Audio-Speech Co-Reasoning (JASCO), a novel task that unifies audio and speech processing, strictly requiring co-reasoning across both modalities. We also release a scene-reasoning dataset called"What Are They Doing". Additionally, we provide deeper insights into the models' behaviors by analyzing their dependence on each modality.
We present a comprehensive explainability dashboard designed for in-game chat toxicity. This dashboard integrates various existing explainab… (voir plus)le AI (XAI) techniques, including token importance analysis, model output visualization, and attribution to the training dataset. It also provides insights through the closest positive and negative examples, facilitating a deeper understanding and potential correction of the training data. Additionally, the dashboard includes word sense analysis—particularly useful for new moderators—and offers free-text explanations for both positive and negative predictions. This multi-faceted approach enhances the interpretability and transparency of toxicity detection models.
As large language models (LLMs) advance, their potential applications have grown significantly. However, it remains difficult to evaluate LL… (voir plus)M behavior on user-specific tasks and craft effective pipelines to do so. Many users struggle with where to start, often referred to as the"blank page"problem. ChainBuddy, an AI assistant for generating evaluative LLM pipelines built into the ChainForge platform, aims to tackle this issue. ChainBuddy offers a straightforward and user-friendly way to plan and evaluate LLM behavior, making the process less daunting and more accessible across a wide range of possible tasks and use cases. We report a within-subjects user study comparing ChainBuddy to the baseline interface. We find that when using AI assistance, participants reported a less demanding workload and felt more confident setting up evaluation pipelines of LLM behavior. We derive insights for the future of interfaces that assist users in the open-ended evaluation of AI.
