arXiv subjects

Anshuman Mishra

Publications and source records attributed to Anshuman Mishra.

6 recordsLinked to original sources

arXiv2026

Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer

We show that information can be transferred post-hoc across independently trained health foundation models (FMs), each pretrained on ~20M minutes of wearable sensor data from ~172K participants, by aligning their data-dependent coordinate systems. From frozen embeddings we extract candidate symbol-like components using linear decomposition methods, and align them across models with simple linear maps. Aligned symbols associate selectively with health conditions and physiological attributes, with associations similar across modalities and architectures. A classifier trained on one model's symbols and applied to another retains more than 95% of its in-domain performance, with similar retention in both directions. Overall, our results indicate that independently trained health FMs converge toward a common representation of the same underlying physiology.

cs.LG↗

arXiv2025

Wearable Accelerometer Foundation Models for Health via Knowledge Distillation

Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all biosignals are the same: high-fidelity biosignals, such as photoplethysmogram (PPG), contain more physiological information, but require optical sensors with a high power footprint. Alternatively, a lower-fidelity biosignal such as accelerometry has a significantly smaller power footprint and is available in almost any wearable device. While accelerometry is widely used for activity recognition and fitness, it is less explored for health biomarkers and diagnosis. Here, we show that an accelerometry foundation model can predict a wide variety of health targets. To achieve improved performance, we distill representational knowledge from PPG encoders to accelerometery encoders using 20 million minutes of unlabeled data, collected from ~172K participants in the Apple Heart and Movement Study under informed consent. We observe strong cross-modal alignment on unseen data, e.g., 99.2% top-1 accuracy for retrieving PPG embeddings from accelerometry embeddings. We show that distilled accelerometry encoders have significantly more informative representations compared to self-supervised or supervised encoders trained directly on accelerometry data, observed by at least 23%-49% improved performance for predicting heart rate and heart rate variability. We also show that distilled accelerometry encoders are readily predictive of a wide array of downstream health targets, i.e., they are generalist foundation models. We believe accelerometry foundation models for health may unlock new opportunities for developing digital biomarkers from any wearable device.

cs.LG↗

arXiv2024

KerasCV and KerasNLP: Vision and Language Power-Ups

We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch. These domain packages are designed to enable fast experimentation, with a focus on ease-of-use and performance. We adopt a modular, layered design: at the library's lowest level of abstraction, we provide building blocks for creating models and data preprocessing pipelines, and at the library's highest level of abstraction, we provide pretrained ``task" models for popular architectures such as Stable Diffusion, YOLOv8, GPT2, BERT, Mistral, CLIP, Gemma, T5, etc. Task models have built-in preprocessing, pretrained weights, and can be fine-tuned on raw inputs. To enable efficient training, we support XLA compilation for all models, and run all preprocessing via a compiled graph of TensorFlow operations using the tf.data API. The libraries are fully open-source (Apache 2.0 license) and available on GitHub.

cs.AI↗

arXiv2023

Smart Watch Supported System for Health Care Monitoring

This work presents a smartwatch attached to patients at remote locations, which would help in the navigation of wheel chair and monitor the vitals of patients and relay it through IoT. This wearable smartwatch is equipped with sensors to measure health parameters, namely, heartbeat, blood pressure, body temperature, and step count. An esp8266 Wi-Fi module uploads the health parameters into the thingspeak cloud platform with a time stamp. This smartwatch is equipped with a joystick for cruise and navigation control of the motor driver-enabled wheelchair. Additionally, an ultrasonic sensor mounted in front of the wheelchair continuously scans for any obstacles ahead and stops the motion of the wheelchair upon detection of an obstacle. The primary controller of the system is an Arduino UNO microcontroller, which interfaces the input and output modules.

eess.SY↗

arXiv2020

Reading Comprehension as Natural Language Inference: A Semantic Analysis

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.

cs.CL↗

arXiv2020

Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks

In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it. However, the full promise of NLI -- particularly that it learns knowledge that should be generalizable to other downstream NLP tasks -- has not been realized. In this paper, we study this unfulfilled promise from the lens of two downstream tasks: question answering (QA), and text summarization. We conjecture that a key difference between the NLI datasets and these downstream tasks concerns the length of the premise; and that creating new long premise NLI datasets out of existing QA datasets is a promising avenue for training a truly generalizable NLI model. We validate our conjecture by showing competitive results on the task of QA and obtaining the best reported results on the task of Checking Factual Correctness of Summaries.

cs.CL↗