pwc-archive/papers-with-abstracts huggingface dataset:
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
Authors: Ryan J. Urbanowicz, Harsh Bandhey, Brendan T. Keenan, Greg Maislin, Sy Hwang, Danielle L. Mowery, Shannon M. Lynch, Diego R. Mazzotti, Fang Han, Qing Yun Li, Thomas Penzel, Sergio Tufik, Lia Bittencourt, Thorarinn Gislason, Philip de Chazal, Bhajan Singh, Nigel McArdle, Ning-Hung Chen, Allan Pack, Richard J. Schwab, Peter A. Cistulli, Ulysses J. Magalang
Date: 2023-12-09 00:00:00
Tasks: Anatomy, AutoML, Benchmarking, Binary Classification

While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA); a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. STREAMLINE is designed to tackle biomedical binary classification tasks while adhering to best practices and accommodating complexity, scalability, reproducibility, customization, and model interpretation. Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was observed with the addition of each subsequent feature set to predict any OSA, with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA data suggests that CF features provide additional value in predicting moderate/severe OSA, but neither CF nor IO features meaningfully improved the prediction of any OSA beyond established demographics, comorbidity and symptom characteristics.
Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal
Authors: Gusti Ahmad Fanshuri Alfarisy, Owais Ahmed Malik, Ong Wee Hong
Date: 2023-12-09 00:00:00
Tasks: AutoML, Transfer Learning

Open-World Recognition (OWR) is an emerging field that makes a machine learning model competent in rejecting the unknowns, managing them, and incrementally adding novel samples to the base knowledge. However, this broad objective is not practical for an agent that works on a specific task. Not all rejected samples will be used for learning continually in the future. Some novel images in the open environment may not belong to the domain of interest. Hence, identifying the unknown in the domain of interest is essential for a machine learning model to learn merely the important samples. In this study, we propose an evaluation protocol for estimating a model's capability in separating unknown in-domain (ID) and unknown out-of-domain (OOD). We evaluated using three approaches with an unknown domain and demonstrated the possibility of identifying the domain of interest using the pre-trained parameters through traditional transfer learning, Automated Machine Learning (AutoML), and Nearest Class Mean (NCM) classifier with First Integer Neighbor Clustering Hierarchy (FINCH). We experimented with five different domains: garbage, food, dogs, plants, and birds. The results show that all approaches can be used as an initial baseline yielding a good accuracy. In addition, a Balanced Accuracy (BACCU) score from a pre-trained model indicates a tendency to excel in one or more domains of interest. We observed that MobileNetV3 yielded the highest BACCU score for the garbage domain and surpassed complex models such as the transformer network. Meanwhile, our results also suggest that a strong representation in the pre-trained model is important for identifying unknown classes in the same domain. This study could open the bridge toward open-world recognition in domain-specific tasks where the relevancy of the unknown classes is vital.
Consistency Models for Scalable and Fast Simulation-Based Inference
Authors: Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T Radev
Date: 2023-12-09 00:00:00
Tasks: Denoising

Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with this goal, we present consistency models for posterior estimation (CMPE), a new conditional sampler for SBI that inherits the advantages of recent unconstrained architectures and overcomes their sampling inefficiency at inference time. CMPE essentially distills a continuous probability flow and enables rapid few-shot inference with an unconstrained architecture that can be flexibly tailored to the structure of the estimation problem. We provide hyperparameters and default architectures that support consistency training over a wide range of different dimensions, including low-dimensional ones which are important in SBI workflows but were previously difficult to tackle even with unconditional consistency models. Our empirical evaluation demonstrates that CMPE not only outperforms current state-of-the-art algorithms on hard low-dimensional benchmarks, but also achieves competitive performance with much faster sampling speed on two realistic estimation problems with high data and/or parameter dimensions.
Rate-Distortion-Perception Theory for Semantic Communication
Authors: Jingxuan Chai, Yong Xiao, Guangming Shi, Walid Saad
Date: 2023-12-09 00:00:00
Tasks: Semantic Communication

Semantic communication has attracted significant interest recently due to its capability to meet the fast growing demand on user-defined and human-oriented communication services such as holographic communications, eXtended reality (XR), and human-to-machine interactions. Unfortunately, recent study suggests that the traditional Shannon information theory, focusing mainly on delivering semantic-agnostic symbols, will not be sufficient to investigate the semantic-level perceptual quality of the recovered messages at the receiver. In this paper, we study the achievable data rate of semantic communication under the symbol distortion and semantic perception constraints. Motivated by the fact that the semantic information generally involves rich intrinsic knowledge that cannot always be directly observed by the encoder, we consider a semantic information source that can only be indirectly sensed by the encoder. Both encoder and decoder can access to various types of side information that may be closely related to the user's communication preference. We derive the achievable region that characterizes the tradeoff among the data rate, symbol distortion, and semantic perception, which is then theoretically proved to be achievable by a stochastic coding scheme. We derive a closed-form achievable rate for binary semantic information source under any given distortion and perception constraints. We observe that there exists cases that the receiver can directly infer the semantic information source satisfying certain distortion and perception constraints without requiring any data communication from the transmitter. Experimental results based on the image semantic source signal have been presented to verify our theoretical observations.
Stochastic Directly-Follows Process Discovery Using Grammatical Inference
Authors: Hanan Alkhammash, Artem Polyvyanyy, Alistair Moffat
Date: 2023-12-09 00:00:00

Starting with a collection of traces generated by process executions, process discovery is the task of constructing a simple model that describes the process, where simplicity is often measured in terms of model size. The challenge of process discovery is that the process of interest is unknown, and that while the input traces constitute positive examples of process executions, no negative examples are available. Many commercial tools discover Directly-Follows Graphs, in which nodes represent the observable actions of the process, and directed arcs indicate execution order possibilities over the actions. We propose a new approach for discovering sound Directly-Follows Graphs that is grounded in grammatical inference over the input traces. To promote the discovery of small graphs that also describe the process accurately we design and evaluate a genetic algorithm that supports the convergence of the inference parameters to the areas that lead to the discovery of interesting models. Experiments over real-world datasets confirm that our new approach can construct smaller models that represent the input traces and their frequencies more accurately than the state-of-the-art technique. Reasoning over the frequencies of encoded traces also becomes possible, due to the stochastic semantics of the action graphs we propose, which, for the first time, are interpreted as models that describe the stochastic languages of action traces.
Efficient Quantization Strategies for Latent Diffusion Models
Authors: Yuewei Yang, Xiaoliang Dai, Jialiang Wang, Peizhao Zhang, Hongbo Zhang
Date: 2023-12-09 00:00:00
Tasks: Image Generation, Quantization, Text to Image Generation, Text-to-Image Generation

Latent Diffusion Models (LDMs) capture the dynamic evolution of latent variables over time, blending patterns and multimodality in a generative system. Despite the proficiency of LDM in various applications, such as text-to-image generation, facilitated by robust text encoders and a variational autoencoder, the critical need to deploy large generative models on edge devices compels a search for more compact yet effective alternatives. Post Training Quantization (PTQ), a method to compress the operational size of deep learning models, encounters challenges when applied to LDM due to temporal and structural complexities. This study proposes a quantization strategy that efficiently quantize LDMs, leveraging Signal-to-Quantization-Noise Ratio (SQNR) as a pivotal metric for evaluation. By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies. The global quantization process mitigates relative quantization noise by initiating higher-precision quantization on sensitive blocks, while local treatments address specific challenges in quantization-sensitive and time-sensitive modules. The outcomes of our experiments reveal that the implementation of both global and local treatments yields a highly efficient and effective Post Training Quantization (PTQ) of LDMs.
Large-scale Training of Foundation Models for Wearable Biosignals
Authors: Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller, Saba Emrani, Udhyakumar Nallasamy, Ian Shapiro
Date: 2023-12-08 00:00:00
Tasks: Photoplethysmography (PPG), Self-Supervised Learning

Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions. Today, wearable devices can conveniently record various biosignals, creating the opportunity to monitor health status without disruption to one's daily routine. Despite widespread use of wearable devices and existing digital biomarkers, the absence of curated data with annotated medical labels hinders the development of new biomarkers to measure common health conditions. In fact, medical datasets are usually small in comparison to other domains, which is an obstacle for developing neural network models for biosignals. To address this challenge, we have employed self-supervised learning using the unlabeled sensor data collected under informed consent from the large longitudinal Apple Heart and Movement Study (AHMS) to train foundation models for two common biosignals: photoplethysmography (PPG) and electrocardiogram (ECG) recorded on Apple Watch. We curated PPG and ECG datasets from AHMS that include data from ~141K participants spanning ~3 years. Our self-supervised learning framework includes participant level positive pair selection, stochastic augmentation module and a regularized contrastive loss optimized with momentum training, and generalizes well to both PPG and ECG modalities. We show that the pre-trained foundation models readily encode information regarding participants' demographics and health conditions. To the best of our knowledge, this is the first study that builds foundation models using large-scale PPG and ECG data collected via wearable consumer devices $\unicode{x2013}$ prior works have commonly used smaller-size datasets collected in clinical and experimental settings. We believe PPG and ECG foundation models can enhance future wearable devices by reducing the reliance on labeled data and hold the potential to help the users improve their health.
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
Authors: Debo Cheng, Yang Xie, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Yinghao Zhang, Zaiwen Feng
Date: 2023-12-08 00:00:00
Tasks: Causal Inference, Representation Learning

In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observational data, for example, confounding bias and M-bias. Recent data-driven causal effect estimators tackle the confounding bias problem via balanced representation learning, but assume no M-bias in the system, thus they fail to handle the M-bias. In this paper, we identify a challenging and unsolved problem caused by a variable that leads to confounding bias and M-bias simultaneously. To address this problem with co-occurring M-bias and confounding bias, we propose a novel Disentangled Latent Representation learning framework for learning latent representations from proxy variables for unbiased Causal effect Estimation (DLRCE) from observational data. Specifically, DLRCE learns three sets of latent representations from the measured proxy variables to adjust for the confounding bias and M-bias. Extensive experiments on both synthetic and three real-world datasets demonstrate that DLRCE significantly outperforms the state-of-the-art estimators in the case of the presence of both confounding bias and M-bias.
The logic of NTQR evaluations of noisy AI agents: Complete postulates and logically consistent error correlations
Authors: Andrés Corrada-Emmanuel
Date: 2023-12-08 00:00:00

In his "ship of state" allegory (\textit{Republic}, Book VI, 488) Plato poses a question -- how can a crew of sailors presumed to know little about the art of navigation recognize the true pilot among them? The allegory argues that a simple majority voting procedure cannot safely determine who is most qualified to pilot a ship when the voting members are ignorant or biased. We formalize Plato's concerns by considering the problem in AI safety of monitoring noisy AI agents in unsupervised settings. An algorithm evaluating AI agents using unlabeled data would be subject to the evaluation dilemma - how would we know the evaluation algorithm was correct itself? This endless validation chain can be avoided by considering purely algebraic functions of the observed responses. We can construct complete postulates than can prove or disprove the logical consistency of any grading algorithm. A complete set of postulates exists whenever we are evaluating $N$ experts that took $T$ tests with $Q$ questions with $R$ responses each. We discuss evaluating binary classifiers that have taken a single test - the $(N,T=1,Q,R=2)$ tests. We show how some of the postulates have been previously identified in the ML literature but not recognized as such - the \textbf{agreement equations} of Platanios. The complete postulates for pair correlated binary classifiers are considered and we show how it allows for error correlations to be quickly calculated. An algebraic evaluator based on the assumption that the ensemble is error independent is compared with grading by majority voting on evaluations using the \uciadult and and \texttt{two-norm} datasets. Throughout, we demonstrate how the formalism of logical consistency via algebraic postulates of evaluation can help increase the safety of machines using AI algorithms.
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels
Authors: Bei Zhou, Soren Riis
Date: 2023-12-08 00:00:00
Tasks: Learning with noisy labels, Reinforcement Learning (RL)

This paper delves into applying reinforcement learning (RL) in strategy games, particularly those characterized by parity challenges, as seen in specific positions of Go and Chess and a broader range of impartial games. We propose a simulated learning process, structured within a curriculum learning framework and augmented with noisy labels, to mirror the intricacies of self-play learning scenarios. This approach thoroughly analyses how neural networks (NNs) adapt and evolve from elementary to increasingly complex game positions. Our empirical research indicates that even minimal label noise can significantly impede NNs' ability to discern effective strategies, a difficulty that intensifies with the growing complexity of the game positions. These findings underscore the urgent need for advanced methodologies in RL training, specifically tailored to counter the obstacles imposed by noisy evaluations. The development of such methodologies is crucial not only for enhancing NN proficiency in strategy games with significant parity elements but also for broadening the resilience and efficiency of RL systems across diverse and complex environments.
Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data
Authors: Ville Heilala, Sami Lehesvuori, Raija Hämäläinen, Tommi Kärkkäinen
Date: 2023-12-08 00:00:00

This study employed multimodal learning analytics (MMLA) to analyze behavioral dynamics during the ABCDE procedure in nursing education, focusing on gaze entropy, hand movement velocities, and proximity measures. Utilizing accelerometers and eye-tracking techniques, behaviorgrams were generated to depict various procedural phases. Results identified four primary phases characterized by distinct patterns of visual attention, hand movements, and proximity to the patient or instruments. The findings suggest that MMLA can offer valuable insights into procedural competence in medical education. This research underscores the potential of MMLA to provide detailed, objective evaluations of clinical procedures and their inherent complexities.
Emergence and Function of Abstract Representations in Self-Supervised Transformers
Authors: Quentin RV. Ferry, Joshua Ching, Takashi Kawai
Date: 2023-12-08 00:00:00
Tasks: Decision Making, Navigate

Human intelligence relies in part on our brains' ability to create abstract mental models that succinctly capture the hidden blueprint of our reality. Such abstract world models notably allow us to rapidly navigate novel situations by generalizing prior knowledge, a trait deep learning systems have historically struggled to replicate. However, the recent shift from supervised to self-supervised objectives, combined with expressive transformer-based architectures, have yielded powerful foundation models that appear to learn versatile representations that can support a wide range of downstream tasks. This promising development raises the intriguing possibility of such models developing in silico abstract world models. We test this hypothesis by studying the inner workings of small-scale transformers trained to reconstruct partially masked visual scenes generated from a simple blueprint. We show that the network develops intermediate abstract representations, or abstractions, that encode all semantic features of the dataset. These abstractions manifest as low-dimensional manifolds where the embeddings of semantically related tokens transiently converge, thus allowing for the generalization of downstream computations. Using precise manipulation experiments, we demonstrate that abstractions are central to the network's decision-making process. Our research also suggests that these abstractions are compositionally structured, exhibiting features like contextual independence and part-whole relationships that mirror the compositional nature of the dataset. Finally, we introduce a Language-Enhanced Architecture (LEA) designed to encourage the network to articulate its computations. We find that LEA develops an abstraction-centric language that can be easily interpreted, allowing us to more readily access and steer the network's decision-making process.
Ignorance is Bliss: Robust Control via Information Gating
Authors: Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman
Date: 2023-03-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Inductive Bias, Q-Learning

Informational parsimony provides a useful inductive bias for learning representations that achieve better generalization by being robust to noise and spurious correlations. We propose \textit{information gating} as a way to learn parsimonious representations that identify the minimal information required for a task. When gating information, we can learn to reveal as little information as possible so that a task remains solvable, or hide as little information as possible so that a task becomes unsolvable. We gate information using a differentiable parameterization of the signal-to-noise ratio, which can be applied to arbitrary values in a network, e.g., erasing pixels at the input layer or activations in some intermediate layer. When gating at the input layer, our models learn which visual cues matter for a given task. When gating intermediate layers, our models learn which activations are needed for subsequent stages of computation. We call our approach \textit{InfoGating}. We apply InfoGating to various objectives such as multi-step forward and inverse dynamics models, Q-learning, and behavior cloning, highlighting how InfoGating can naturally help in discarding information not relevant for control. Results show that learning to identify and use minimal information can improve generalization in downstream tasks. Policies based on InfoGating are considerably more robust to irrelevant visual features, leading to improved pretraining and finetuning of RL models.