Self Supervised Dictionary Learning Using Kernel Matching
Shubham Choudhary
Demba Ba
We introduce a self supervised framework for learning representations in the context of dictionary learning. We cast the problem as a kernel… (see more) 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.
What Are They Doing? Joint Audio-Speech Co-Reasoning
Yingzhi Wang
Pooneh Mousavi
Artem Ploujnikov
In audio and speech processing, tasks usually focus on either the audio or speech modality, even when both sounds and human speech are prese… (see more)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.
What Are They Doing? Joint Audio-Speech Co-Reasoning
Yingzhi Wang
Pooneh Mousavi
Artem Ploujnikov
AI content detection in the emerging information ecosystem: new obligations for media and tech companies
Alistair Knott
Dino Pedreschi
Toshiya Jitsuzumi
Susan Leavy
D. Eyers
Tapabrata Chakraborti
Andrew Trotman
Sundar Sundareswaran
Ricardo Baeza-Yates
Przemyslaw Biecek
Adrian Weller
Paul D. Teal
Subhadip Basu
Mehmet Haklidir
Virginia Morini
Stuart Russell
ToxiSight: Insights Towards Detected Chat Toxicity
Zachary Yang
Domenico Tullo
We present a comprehensive explainability dashboard designed for in-game chat toxicity. This dashboard integrates various existing explainab… (see more)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.
ChainBuddy: An AI Agent System for Generating LLM Pipelines
Jingyue Zhang
As large language models (LLMs) advance, their potential applications have grown significantly. However, it remains difficult to evaluate LL… (see more)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.
ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines
Jingyue Zhang
ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines
Jingyue Zhang
Development of small, cost‐efficient scintillating fiber detectors for automated synthesis of positron emission tomography radiopharmaceuticals
Hailey Ahn
Liam Carroll
Robert Hopewell
I-Huang Tsai
Dean Jolly
Gassan Massarweh
Diagnostic tests for infections in critically ill immunocompromised patients
Adrien Joseph
Lara Zafrani
Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming
Shubham Gupta
Isaac Neri Gomez-Sarmiento
Faez Amjed Mezdari
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Jiaming Zhou
Abbas Ghaddar
Ge Zhang
Liheng Ma
Yaochen Hu
Soumyasundar Pal
Bin Wang
Yingxue Zhang
Jianye Hao