Geometry-Aware Generative Autoencoders for Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun
Danqi Liao
Kincaid MacDonald
Yanlei Zhang
Ian Adelstein
Tim G. J. Rudner
Non-linear dimensionality reduction methods have proven successful at learning low-dimensional representations of high-dimensional point clo… (see more)uds on or near data manifolds. However, existing methods are not easily extensible—that is, for large datasets, it is prohibitively expensive to add new points to these embeddings. As a result, it is very difficult to use existing embeddings generatively, to sample new points on and along these manifolds. In this paper, we propose GAGA (geometry-aware generative autoencoders) a framework which merges the power of generative deep learning with non-linear manifold learning by: 1) learning generalizable geometry-aware neural network embeddings based on non-linear dimensionality reduction methods like PHATE and diffusion maps, 2) deriving a non-euclidean pullback metric on the embedded space to generate points faithfully along manifold geodesics, and 3) learning a flow on the manifold that allows us to transport populations. We provide illustration on easily-interpretable synthetic datasets and showcase results on simulated and real single cell datasets. In particular, we show that the geodesic-based generation can be especially important for scientific datasets where the manifold represents a state space and geodesics can represent dynamics of entities over this space.
Is a Good Description Worth a Thousand Pictures? Reducing Multimodal Alignment to Text-Based, Unimodal Alignment
Touraj Laleh
Ardavan S. Nobandegani
Generative AI systems (ChatGPT, Llama, etc.) are increasingly adopted across a range of high-stake domains, including healthcare and crimina… (see more)l justice system. This rapid adoption indeed raises moral and ethical concerns. The emerging field of AI alignment aims to make AI systems that respect human values. In this work, we focus on evaluating the ethics of multimodal AI systems involving both text and images --- a relatively under-explored area, as most alignment work is currently focused on language models. Specifically, here we investigate whether the multimodal alignment problem (i.e., the problem of aligning a multimodal system) could be effectively reduced to the (text-based) unimodal alignment problem, wherein a language model would make a moral judgment purely based on a description of an image. Focusing on GPT-4 and LLaVA as two prominent examples of multimodal systems, here we demonstrate, rather surprisingly, that this reduction can be achieved with a relatively small loss in moral judgment performance in the case of LLaVa, and virtually no loss in the case of GPT-4.
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
IDs for AI Systems
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
Ali Ramlaoui
Basile Terver
Victor Schmidt
Fragkiskos D. Malliaros
Alexandre AGM Duval
Joint Multimodal Transformer for Emotion Recognition in the Wild
Paul Waligora
Muhammad Haseeb Aslam
Muhammad Osama Zeeshan
Soufiane Belharbi
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter-and intra-modal relationships be… (see more)tween, e.g., visual, textual, physiological, and auditory modalities. This paper proposes an MMER method that relies on a joint multi-modal transformer (JMT) for fusion with key-based cross-attention. This framework can exploit the complementary nature of diverse modalities to improve predictive accuracy. Separate backbones capture intra-modal spatiotemporal dependencies within each modality over video sequences. Subsequently, our JMT fusion architecture integrates the individual modality embeddings, allowing the model to effectively capture inter- and intra-modal relationships. Extensive experiments on two challenging expression recognition tasks – (1) dimensional emotion recognition on the Affwild2 dataset (with face and voice) and (2) pain estimation on the Biovid dataset (with face and biosensors) – indicate that our JMT fusion can provide a cost-effective solution for MMER. Empirical results show that MMER systems with our proposed fusion allow us to outperform relevant baseline and state-of-the-art methods. Code is available at: https://github.com/PoloWlg/Joint-Multimodal-Transformer-6th-ABAW