pwc-archive/papers-with-abstracts huggingface dataset:
Memory-Efficient Fine-Tuning of Transformers via Token Selection
Authors: Antoine Simoulin, Namyong Park, Xiaoyi Liu, Grey Yang
Date: 2025-01-31 00:00:00
Tasks: Few-Shot Learning, Question Answering, text-classification, Text Classification

Fine-tuning provides an effective means to specialize pre-trained models for various downstream tasks. However, fine-tuning often incurs high memory overhead, especially for large transformer-based models, such as LLMs. While existing methods may reduce certain parts of the memory required for fine-tuning, they still require caching all intermediate activations computed in the forward pass to update weights during the backward pass. In this work, we develop TokenTune, a method to reduce memory usage, specifically the memory to store intermediate activations, in the fine-tuning of transformer-based models. During the backward pass, TokenTune approximates the gradient computation by backpropagating through just a subset of input tokens. Thus, with TokenTune, only a subset of intermediate activations are cached during the forward pass. Also, TokenTune can be easily combined with existing methods like LoRA, further reducing the memory cost. We evaluate our approach on pre-trained transformer models with up to billions of parameters, considering the performance on multiple downstream tasks such as text classification and question answering in a few-shot learning setup. Overall, TokenTune achieves performance on par with full fine-tuning or representative memory-efficient fine-tuning methods, while greatly reducing the memory footprint, especially when combined with other methods with complementary memory reduction mechanisms. We hope that our approach will facilitate the fine-tuning of large transformers, in specializing them for specific domains or co-training them with other neural components from a larger system. Our code is available at https://github.com/facebookresearch/tokentune.
Transcoders Beat Sparse Autoencoders for Interpretability
Authors: Gonçalo Paulo, Stepan Shabalin, Nora Belrose
Date: 2025-01-31 00:00:00

Sparse autoencoders (SAEs) extract human-interpretable features from deep neural networks by transforming their activations into a sparse, higher dimensional latent space, and then reconstructing the activations from these latents. Transcoders are similar to SAEs, but they are trained to reconstruct the output of a component of a deep network given its input. In this work, we compare the features found by transcoders and SAEs trained on the same model and data, finding that transcoder features are significantly more interpretable. We also propose _skip transcoders_, which add an affine skip connection to the transcoder architecture, and show that these achieve lower reconstruction loss with no effect on interpretability.
Estimating the Probability of Sampling a Trained Neural Network at Random
Authors: Adam Scherlis, Nora Belrose
Date: 2025-01-31 00:00:00
Tasks: Inductive Bias, Language Modeling, Language Modelling

We present an algorithm for estimating the probability mass, under a Gaussian or uniform prior, of a region in neural network parameter space corresponding to a particular behavior, such as achieving test loss below some threshold. When the prior is uniform, this problem is equivalent to measuring the volume of a region. We show empirically and theoretically that existing algorithms for estimating volumes in parameter space underestimate the true volume by millions of orders of magnitude. We find that this error can be dramatically reduced, but not entirely eliminated, with an importance sampling method using gradient information that is already provided by popular optimizers. The negative logarithm of this probability can be interpreted as a measure of a network's information content, in accordance with minimum description length (MDL) principles and rate-distortion theory. As expected, this quantity increases during language model training. We also find that badly-generalizing behavioral regions are smaller, and therefore less likely to be sampled at random, demonstrating an inductive bias towards well-generalizing functions.
Learning Hamiltonian Dynamics with Bayesian Data Assimilation
Authors: Taehyeun Kim, Tae-Geun Kim, Anouck Girard, Ilya Kolmanovsky
Date: 2025-01-31 00:00:00
Tasks: Prediction, Time Series, Time Series Prediction

In this paper, we develop a neural network-based approach for time-series prediction in unknown Hamiltonian dynamical systems. Our approach leverages a surrogate model and learns the system dynamics using generalized coordinates (positions) and their conjugate momenta while preserving a constant Hamiltonian. To further enhance long-term prediction accuracy, we introduce an Autoregressive Hamiltonian Neural Network, which incorporates autoregressive prediction errors into the training objective. Additionally, we employ Bayesian data assimilation to refine predictions in real-time using online measurement data. Numerical experiments on a spring-mass system and highly elliptic orbits under gravitational perturbations demonstrate the effectiveness of the proposed method, highlighting its potential for accurate and robust long-term predictions.
Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion
Authors: Vitor Guizilini, Muhammad Zubair Irshad, Dian Chen, Greg Shakhnarovich, Rares Ambrus
Date: 2025-01-30 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Scene Reconstruction, Depth Estimation, Novel View Synthesis

Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-view consistent scene appearance and geometry. In this paper we introduce MVGD, a diffusion-based architecture capable of direct pixel-level generation of images and depth maps from novel viewpoints, given an arbitrary number of input views. Our method uses raymap conditioning to both augment visual features with spatial information from different viewpoints, as well as to guide the generation of images and depth maps from novel views. A key aspect of our approach is the multi-task generation of images and depth maps, using learnable task embeddings to guide the diffusion process towards specific modalities. We train this model on a collection of more than 60 million multi-view samples from publicly available datasets, and propose techniques to enable efficient and consistent learning in such diverse conditions. We also propose a novel strategy that enables the efficient training of larger models by incrementally fine-tuning smaller ones, with promising scaling behavior. Through extensive experiments, we report state-of-the-art results in multiple novel view synthesis benchmarks, as well as multi-view stereo and video depth estimation.
Deceptive Sequential Decision-Making via Regularized Policy Optimization
Authors: Yerin Kim, Alexander Benvenuti, Bo Chen, Mustafa Karabag, Abhishek Kulkarni, Nathaniel D. Bastian, Ufuk Topcu, Matthew Hale
Date: 2025-01-30 00:00:00
Tasks: Decision Making, Sequential Decision Making

Autonomous systems are increasingly expected to operate in the presence of adversaries, though an adversary may infer sensitive information simply by observing a system, without even needing to interact with it. Therefore, in this work we present a deceptive decision-making framework that not only conceals sensitive information, but in fact actively misleads adversaries about it. We model autonomous systems as Markov decision processes, and we consider adversaries that attempt to infer their reward functions using inverse reinforcement learning. To counter such efforts, we present two regularization strategies for policy synthesis problems that actively deceive an adversary about a system's underlying rewards. The first form of deception is ``diversionary'', and it leads an adversary to draw any false conclusion about what the system's reward function is. The second form of deception is ``targeted'', and it leads an adversary to draw a specific false conclusion about what the system's reward function is. We then show how each form of deception can be implemented in policy optimization problems, and we analytically bound the loss in total accumulated reward that is induced by deception. Next, we evaluate these developments in a multi-agent sequential decision-making problem with one real agent and multiple decoys. We show that diversionary deception can cause the adversary to believe that the most important agent is the least important, while attaining a total accumulated reward that is $98.83\%$ of its optimal, non-deceptive value. Similarly, we show that targeted deception can make any decoy appear to be the most important agent, while still attaining a total accumulated reward that is $99.25\%$ of its optimal, non-deceptive value.
Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble
Authors: HanYang Wang, Juergen Branke, Matthias Poloczek
Date: 2025-01-30 00:00:00
Tasks: Bayesian Optimization

Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive preference learning allows to focus the search on the most relevant subset. However, few previous studies have exploited the fact that utility functions are usually monotonic. In this paper, we address the Bayesian Optimization with Preference Exploration (BOPE) problem and propose using a neural network ensemble as a utility surrogate model. This approach naturally integrates monotonicity and supports pairwise comparison data. Our experiments demonstrate that the proposed method outperforms state-of-the-art approaches and exhibits robustness to noise in utility evaluations. An ablation study highlights the critical role of monotonicity in enhancing performance.
Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models
Authors: Alessio Russo, Alberto Maria Metelli, Marcello Restelli
Date: 2025-01-30 00:00:00

We tackle average-reward infinite-horizon POMDPs with an unknown transition model but a known observation model, a setting that has been previously addressed in two limiting ways: (i) frequentist methods relying on suboptimal stochastic policies having a minimum probability of choosing each action, and (ii) Bayesian approaches employing the optimal policy class but requiring strong assumptions about the consistency of employed estimators. Our work removes these limitations by proving convenient estimation guarantees for the transition model and introducing an optimistic algorithm that leverages the optimal class of deterministic belief-based policies. We introduce modifications to existing estimation techniques providing theoretical guarantees separately for each estimated action transition matrix. Unlike existing estimation methods that are unable to use samples from different policies, we present a novel and simple estimator that overcomes this barrier. This new data-efficient technique, combined with the proposed \emph{Action-wise OAS-UCRL} algorithm and a tighter theoretical analysis, leads to the first approach enjoying a regret guarantee of order $\mathcal{O}(\sqrt{T \,\log T})$ when compared against the optimal policy, thus improving over state of the art techniques. Finally, theoretical results are validated through numerical simulations showing the efficacy of our method against baseline methods.
Navigating the Fragrance space Via Graph Generative Models And Predicting Odors
Authors: Mrityunjay Sharma, Sarabeshwar Balaji, Pinaki Saha, Ritesh Kumar
Date: 2025-01-30 00:00:00
Tasks: Navigate

We explore a suite of generative modelling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor likeliness with ROC AUC score of 0.97 and assign probable odor labels. We correlate odor likeliness with physicochemical features of molecules using machine learning techniques and leverage SHAP (SHapley Additive exPlanations) to demonstrate the interpretability of the function. The whole process involves four key stages: molecule generation, stringent sanitization checks for molecular validity, fragrance likeliness screening and odor prediction of the generated molecules. By making our code and trained models publicly accessible, we aim to facilitate broader adoption of our research across applications in fragrance discovery and olfactory research.
Beyond Short Steps in Frank-Wolfe Algorithms
Authors: David Martínez-Rubio, Sebastian Pokutta
Date: 2025-01-30 00:00:00

We introduce novel techniques to enhance Frank-Wolfe algorithms by leveraging function smoothness beyond traditional short steps. Our study focuses on Frank-Wolfe algorithms with step sizes that incorporate primal-dual guarantees, offering practical stopping criteria. We present a new Frank-Wolfe algorithm utilizing an optimistic framework and provide a primal-dual convergence proof. Additionally, we propose a generalized short-step strategy aimed at optimizing a computable primal-dual gap. Interestingly, this new generalized short-step strategy is also applicable to gradient descent algorithms beyond Frank-Wolfe methods. As a byproduct, our work revisits and refines primal-dual techniques for analyzing Frank-Wolfe algorithms, achieving tighter primal-dual convergence rates. Empirical results demonstrate that our optimistic algorithm outperforms existing methods, highlighting its practical advantages.
Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring
Authors: Zijun Deng, Rafael Orozco, Abhinav Prakash Gahlot, Felix J. Herrmann
Date: 2025-01-30 00:00:00
Tasks: Seismic Imaging, Uncertainty Quantification

Reducing CO$_2$ emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO$_2$ emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO$_2$ plume dynamics and reservoir properties. Building on existing seismic imaging methods like the Joint Recovery Method (JRM), which lacks uncertainty quantification, we propose the Probabilistic Joint Recovery Method (pJRM). By estimating posterior distributions across surveys using a shared generative model, pJRM provides uncertainty information to improve risk assessment in CCS projects.
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
Authors: Nuojin Cheng, Leonard Papenmeier, Stephen Becker, Luigi Nardi
Date: 2025-01-30 00:00:00
Tasks: Bayesian Optimization, Variational Inference

Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function's optimum and are often considered fundamentally distinct from EI. In this work, we challenge this prevailing perspective by introducing a unified theoretical framework, Variational Entropy Search, which reveals that EI and information-theoretic acquisition functions are more closely related than previously recognized. We demonstrate that EI can be interpreted as a variational inference approximation of the popular information-theoretic acquisition function, named Max-value Entropy Search. Building on this insight, we propose VES-Gamma, a novel acquisition function that balances the strengths of EI and MES. Extensive empirical evaluations across both low- and high-dimensional synthetic and real-world benchmarks demonstrate that VES-Gamma is competitive with state-of-the-art acquisition functions and in many cases outperforms EI and MES.
STaleX: A Spatiotemporal-Aware Adaptive Auto-scaling Framework for Microservices
Authors: Majid Dashtbani, Ladan Tahvildari
Date: 2025-01-30 00:00:00

While cloud environments and auto-scaling solutions have been widely applied to traditional monolithic applications, they face significant limitations when it comes to microservices-based architectures. Microservices introduce additional challenges due to their dynamic and spatiotemporal characteristics, which require more efficient and specialized auto-scaling strategies. Centralized auto-scaling for the entire microservice application is insufficient, as each service within a chain has distinct specifications and performance requirements. Therefore, each service requires its own dedicated auto-scaler to address its unique scaling needs effectively, while also considering the dependencies with other services in the chain and the overall application. This paper presents a combination of control theory, machine learning, and heuristics to address these challenges. We propose an adaptive auto-scaling framework, STaleX, for microservices that integrates spatiotemporal features, enabling real-time resource adjustments to minimize SLO violations. STaleX employs a set of weighted Proportional-Integral-Derivative (PID) controllers for each service, where weights are dynamically adjusted based on a supervisory unit that integrates spatiotemporal features. This supervisory unit continuously monitors and adjusts both the weights and the resources allocated to each service. Our framework accounts for spatial features, including service specifications and dependencies among services, as well as temporal variations in workload, ensuring that resource allocation is continuously optimized. Through experiments on a microservice-based demo application deployed on a Kubernetes cluster, we demonstrate the effectiveness of our framework in improving performance and reducing costs compared to traditional scaling methods like Kubernetes Horizontal Pod Autoscaler (HPA) with a 26.9% reduction in resource usage.
Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment
Authors: Maria R. Lima, Alexander Capstick, Fatemeh Geranmayeh, Ramin Nilforooshan, Maja Matarić, Ravi Vaidyanathan, Payam Barnaghi
Date: 2025-01-30 00:00:00
Tasks: Feature Importance, Sensitivity, severity prediction, Specificity

Timely and accurate assessment of cognitive impairment is a major unmet need in populations at risk. Alterations in speech and language can be early predictors of Alzheimer's disease and related dementias (ADRD) before clinical signs of neurodegeneration. Voice biomarkers offer a scalable and non-invasive solution for automated screening. However, the clinical applicability of machine learning (ML) remains limited by challenges in generalisability, interpretability, and access to patient data to train clinically applicable predictive models. Using DementiaBank recordings (N=291, 64% female), we evaluated ML techniques for ADRD screening and severity prediction from spoken language. We validated model generalisability with pilot data collected in-residence from older adults (N=22, 59% female). Risk stratification and linguistic feature importance analysis enhanced the interpretability and clinical utility of predictions. For ADRD classification, a Random Forest applied to lexical features achieved a mean sensitivity of 69.4% (95% confidence interval (CI) = 66.4-72.5) and specificity of 83.3% (78.0-88.7). On real-world pilot data, this model achieved a mean sensitivity of 70.0% (58.0-82.0) and specificity of 52.5% (39.3-65.7). For severity prediction using Mini-Mental State Examination (MMSE) scores, a Random Forest Regressor achieved a mean absolute MMSE error of 3.7 (3.7-3.8), with comparable performance of 3.3 (3.1-3.5) on pilot data. Linguistic features associated with higher ADRD risk included increased use of pronouns and adverbs, greater disfluency, reduced analytical thinking, lower lexical diversity and fewer words reflecting a psychological state of completion. Our interpretable predictive modelling offers a novel approach for in-home integration with conversational AI to monitor cognitive health and triage higher-risk individuals, enabling earlier detection and intervention.
Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques
Authors: Anthony Mendil, Felix Putze
Date: 2025-01-30 00:00:00
Tasks: Anatomy, Attribute, Disentanglement

Attribute manipulation deals with the problem of changing individual attributes of a data point or a time series, while leaving all other aspects unaffected. This work focuses on the domain of human motion, more precisely karate movement patterns. To the best of our knowledge, it presents the first success at manipulating attributes of human motion data. One of the key requirements for achieving attribute manipulation on human motion is a suitable pose representation. Therefore, we design a novel rotation-based pose representation that enables the disentanglement of the human skeleton and the motion trajectory, while still allowing an accurate reconstruction of the original anatomy. The core idea of the manipulation approach is to use a transformer encoder for discovering high-level semantics, and a diffusion probabilistic model for modeling the remaining stochastic variations. We show that the embedding space obtained from the transformer encoder is semantically meaningful and linear. This enables the manipulation of high-level attributes, by discovering their linear direction of change in the semantic embedding space and moving the embedding along said direction. The code and data are available at https://github.com/anthony-mendil/MoDiffAE.
Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
Authors: Andrew M Birnbaum, Adam Buchwald, Peter Turkeltaub, Adam Jacks, Yu Huang, Abhisheck Datta, Lucas C Parra, Lukas A Hirsch
Date: 2025-01-30 00:00:00
Tasks: Anatomy, Brain Segmentation, MRI segmentation, Segmentation

The goal of this work was to develop a deep network for whole-head segmentation, including clinical MRIs with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 91 MRIs with volumetric segmentation labels for a diverse set of human subjects (4 normal, 32 traumatic brain injuries, and 57 strokes). These clinical cases are characterized by extended cerebrospinal fluid (CSF) in regions normally containing the brain. Training labels were generated by manually correcting initial automated segmentations for skin/scalp, skull, CSF, gray matter, white matter, air cavity, and extracephalic air. We developed a MultiAxial network consisting of three 2D U-Net models that operate independently in sagittal, axial, and coronal planes and are then combined to produce a single 3D segmentation. The MultiAxial network achieved test-set Dice scores of 0.88 (median plus-minus 0.04). For brain tissue, it significantly outperforms existing brain segmentation methods (MultiAxial: 0.898 plus-minus 0.041, SynthSeg: 0.758 plus-minus 0.054, BrainChop: 0.757 plus-minus 0.125). The MultiAxial network gains in robustness by avoiding the need for coregistration with an atlas. It performed well in regions with abnormal anatomy and on images that have been de-identified. It enables more robust current flow modeling when incorporated into ROAST, a widely-used modeling toolbox for transcranial electric stimulation. We are releasing a state-of-the-art model for whole-head MRI segmentation, along with a dataset of 61 clinical MRIs and training labels, including non-brain structures. Together, the model and data may serve as a benchmark for future efforts.