27 research outputs found
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Pruning is one of the predominant approaches for compressing deep neural
networks (DNNs). Lately, coresets (provable data summarizations) were leveraged
for pruning DNNs, adding the advantage of theoretical guarantees on the
trade-off between the compression rate and the approximation error. However,
coresets in this domain were either data-dependent or generated under
restrictive assumptions on both the model's weights and inputs. In real-world
scenarios, such assumptions are rarely satisfied, limiting the applicability of
coresets. To this end, we suggest a novel and robust framework for computing
such coresets under mild assumptions on the model's weights and without any
assumption on the training data. The idea is to compute the importance of each
neuron in each layer with respect to the output of the following layer. This is
achieved by a combination of L\"{o}wner ellipsoid and Caratheodory theorem. Our
method is simultaneously data-independent, applicable to various networks and
datasets (due to the simplified assumptions), and theoretically supported.
Experimental results show that our method outperforms existing coreset based
neural pruning approaches across a wide range of networks and datasets. For
example, our method achieved a compression rate on ResNet50 on ImageNet
with drop in accuracy
Deep Learning on Home Drone: Searching for the Optimal Architecture
We suggest the first system that runs real-time semantic segmentation via
deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose
price was \16\times\times$ 41 mm). The result is an autonomous drone (no
laptop nor human in the loop) that can detect and classify objects in real-time
from a video stream of an on-board monocular RGB camera (no GPS or LIDAR
sensors). The companion videos demonstrate how this Tello drone scans the lab
for people (e.g. for the use of firefighters or security forces) and for an
empty parking slot outside the lab.
Existing deep learning solutions are either much too slow for real-time
computation on such IoT devices, or provide results of impractical quality. Our
main challenge was to design a system that takes the best of all worlds among
numerous combinations of networks, deep learning platforms/frameworks,
compression techniques, and compression ratios. To this end, we provide an
efficient searching algorithm that aims to find the optimal combination which
results in the best tradeoff between the network running time and its
accuracy/performance
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
As autonomous driving technology matures, end-to-end methodologies have
emerged as a leading strategy, promising seamless integration from perception
to control via deep learning. However, existing systems grapple with challenges
such as unexpected open set environments and the complexity of black-box
models. At the same time, the evolution of deep learning introduces larger,
multimodal foundational models, offering multi-modal visual and textual
understanding. In this paper, we harness these multimodal foundation models to
enhance the robustness and adaptability of autonomous driving systems, enabling
out-of-distribution, end-to-end, multimodal, and more explainable autonomy.
Specifically, we present an approach to apply end-to-end open-set (any
environment/scene) autonomous driving that is capable of providing driving
decisions from representations queryable by image and text. To do so, we
introduce a method to extract nuanced spatial (pixel/patch-aligned) features
from transformers to enable the encapsulation of both spatial and semantic
features. Our approach (i) demonstrates unparalleled results in diverse tests
while achieving significantly greater robustness in out-of-distribution
situations, and (ii) allows the incorporation of latent space simulation (via
text) for improved training (data augmentation via text) and policy debugging.
We encourage the reader to check our explainer video at
https://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.be and to view the
code and demos on our project webpage at https://drive-anywhere.github.io/.Comment: Project webpage: https://drive-anywhere.github.io Explainer video:
https://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.b
Adult Ocular Toxocariasis Mimicking Ciliary Body Malignancy
Purpose. To discuss an unusual presentation of ocular toxocariasis. Methods. Case report. Results. A 40-year-old woman presented with decreased vision in the left eye with a long history of recurrent red eye from uveitis. Eosinophilia and positive ELISA titers for Toxocara canis favored the diagnosis of ocular toxocariasis. Over 3 months, an anterior scleral mass had a rapid growth raising the possibility of medulloepithelioma, which rarely can mimic uveitic syndromes. Surgical plan changed from local excision to enucleation. Histopathology demonstrated a large homogeneous mass of chronic inflammatory cells with inflammation of the overlying thinned out sclera, medial rectus insertion, and limbal cornea. The triad of peripheral granuloma, eosinophilia, and positive blood serology established the diagnosis of ocular toxocariasis. Conclusions. Ocular toxocariasis can mimic ocular malignancy such as medulloepithelioma in adults and rarely presents as an anterior scleral mass