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Found 10 papers, 3 papers with code
Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial
Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues.
Short-lived High-volume Multi-A(rmed)/B(andits) Testing
We aim to minimize the loss due to not knowing the mean rewards, averaged over instances generated from a given prior distribution.
Markdown Pricing Under an Unknown Parametric Demand Model
Without monotonicity, the minimax regret is $\tilde O(n^{2/3})$ for the Lipschitz demand family and $\tilde O(n^{1/2})$ for a general class of parametric demand models.
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
With the proposed integer quantization search, we increase the accuracy of ResNet-18 on ImageNet by 1. 31% points and ResNet-50 by 0. 90% points with equivalent model cost over previous methods.
Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation
In this work, we propose Contrastive X-Ray REport Match (X-REM), a novel retrieval-based radiology report generation module that uses an image-text matching score to measure the similarity of a chest X-ray image and radiology report for report retrieval.
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence.
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
In the problem of active sequential hypothesis testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses.
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments.
Searching for Fast Model Families on Datacenter Accelerators
On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.
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Unseen Object Instance Segmentation
on WISDOM
