Search Results for author:
Found 18 papers, 12 papers with code
Predicting postoperative risks using large language models
Adapting models through self-supervised finetuning further improved performance by 3. 2% for AUROC & 1. 5% for AUPRC Incorporating labels into the finetuning procedure further boosted performances, with semi-supervised finetuning improving by 1. 8% for AUROC & 2% for AUPRC & foundational modelling improving by 3. 6% for AUROC & 2. 6% for AUPRC compared to self-supervised finetuning.
Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints
It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices.
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection
A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome.
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases.
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers
This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Transformers (ViTs).
Self-explaining Hierarchical Model for Intraoperative Time Series
Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series.
Adaptive Edge Offloading for Image Classification Under Rate Limit
Because of limited computing capacity, embedded devices rely on a parsimonious classification model with uneven accuracy.
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records
Burnout is a significant public health concern affecting nearly half of the healthcare workforce.
Self-Supervised Road Layout Parsing with Graph Auto-Encoding
Aiming for higher-level scene understanding, this work presents a neural network approach that takes a road-layout map in bird's-eye-view as input, and predicts a human-interpretable graph that represents the road's topological layout.
Surgical Prediction with Interpretable Latent Representation
Given the risks and cost of surgeries, there has been significant interest in exploiting predictive models to improve perioperative care.
Part-aware Panoptic Segmentation
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing.
Ranked #2 on
Image Segmentation
on Pascal Panoptic Parts
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks
We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery.
Image-Graph-Image Translation via Auto-Encoding
To overcome this, we are the first to present a self-supervised approach based on a fully-differentiable auto-encoder in which the bottleneck encodes the graph's nodes and edges.
Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints
To deploy machine learning-based algorithms for real-time applications with strict latency constraints, we consider an edge-computing setting where a subset of inputs are offloaded to the edge for processing by an accurate but resource-intensive model, and the rest are processed only by a less-accurate model on the device itself.
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
In this technical report, we present two novel datasets for image scene understanding.
Semantic Foreground Inpainting from Weak Supervision
Our approach is inherently more efficient than the previous two-stage state-of-the-art method, and outperforms it by a margin of 3% IoU for the inpainted foreground regions on Cityscapes.
Hallucinating Beyond Observation: Learning to Complete with Partial Observation and Unpaired Prior Knowledge
We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallucination.
Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks
In this work, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid mapping from weak binocular ground truth.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU veh - 224x480 - No vis filter - 100x50 at 0.25 metric)
