arXiv subjects

Eli Laird

Publications and source records attributed to Eli Laird.

3 recordsLinked to original sources

arXiv2026

Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models

World models are typically trained to predict discrete-time physical dynamics with a fixed step size baked into the model weights, preventing prediction at variable temporal resolutions. This matters for hierarchical planning, sim-to-real transfer, and scientific or game-engine applications that must query the same dynamics at multiple timescales. Hamiltonian Generative Networks (HGN) offer a principled path forward, grounding predictions in a continuous-time energy function that is, in principle, independent of the observation frame rate. In practice, however, their temporal generalization breaks down in non-conservative settings. We show that in externally forced, dissipative environments, HGN rollouts at step sizes beyond the training regime fail due to distinct failure modes, including latent magnitude growth driven by an unconstrained action-force map, and global truncation error accumulation from an under-resolved integrator. We identify a targeted fix for each mechanism and demonstrate stable dynamics prediction at temporal resolutions well outside the training distribution. In a detailed analysis, we recommend several strategies for enabling temporal generalization in continuous-time video generation.

cs.LG↗

arXiv2024

SIG: A Synthetic Identity Generation Pipeline for Generating Evaluation Datasets for Face Recognition

As Artificial Intelligence applications expand, the evaluation of models faces heightened scrutiny. Ensuring public readiness requires evaluation datasets, which differ from training data by being disjoint and ethically sourced in compliance with privacy regulations. The performance and fairness of face recognition systems depend significantly on the quality and representativeness of these evaluation datasets. This data is sometimes scraped from the internet without user's consent, causing ethical concerns that can prohibit its use without proper releases. In rare cases, data is collected in a controlled environment with consent, however, this process is time-consuming, expensive, and logistically difficult to execute. This creates a barrier for those unable to conjure the immense resources required to gather ethically sourced evaluation datasets. To address these challenges, we introduce the Synthetic Identity Generation pipeline, or SIG, that allows for the targeted creation of ethical, balanced datasets for face recognition evaluation. Our proposed and demonstrated pipeline generates high-quality images of synthetic identities with controllable pose, facial features, and demographic attributes, such as race, gender, and age. We also release an open-source evaluation dataset named ControlFace10k, consisting of 10,008 face images of 3,336 unique synthetic identities balanced across race, gender, and age, generated using the proposed SIG pipeline. We analyze ControlFace10k along with a non-synthetic BUPT dataset using state-of-the-art face recognition algorithms to demonstrate its effectiveness as an evaluation tool. This analysis highlights the dataset's characteristics and its utility in assessing algorithmic bias across different demographic groups.

cs.CV↗

arXiv2023

XInsight: Revealing Model Insights for GNNs with Flow-based Explanations

Progress in graph neural networks has grown rapidly in recent years, with many new developments in drug discovery, medical diagnosis, and recommender systems. While this progress is significant, many networks are `black boxes' with little understanding of the `what' exactly the network is learning. Many high-stakes applications, such as drug discovery, require human-intelligible explanations from the models so that users can recognize errors and discover new knowledge. Therefore, the development of explainable AI algorithms is essential for us to reap the benefits of AI. We propose an explainability algorithm for GNNs called eXplainable Insight (XInsight) that generates a distribution of model explanations using GFlowNets. Since GFlowNets generate objects with probabilities proportional to a reward, XInsight can generate a diverse set of explanations, compared to previous methods that only learn the maximum reward sample. We demonstrate XInsight by generating explanations for GNNs trained on two graph classification tasks: classifying mutagenic compounds with the MUTAG dataset and classifying acyclic graphs with a synthetic dataset that we have open-sourced. We show the utility of XInsight's explanations by analyzing the generated compounds using QSAR modeling, and we find that XInsight generates compounds that cluster by lipophilicity, a known correlate of mutagenicity. Our results show that XInsight generates a distribution of explanations that uncovers the underlying relationships demonstrated by the model. They also highlight the importance of generating a diverse set of explanations, as it enables us to discover hidden relationships in the model and provides valuable guidance for further analysis.

cs.LG↗