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Learning Efficient Object Detection Models with Knowledge ...
for domains ranging from surveillance to autonomous driving. However, speed is a key requirement in many applications, which fundamentally contends with demands on accuracy. Thus, while advances in object detection have relied on increasingly deeper architectures, they are associated with an increase in computational expense at runtime.
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Generative Adversarial Imitation Learning
proceedings.neurips.ccnetworks [8], a technique from the deep learning community that has led to recent successes in modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where
Prototypical Networks for Few-shot Learning
proceedings.neurips.cc˚: RD!RMwith learnable parameters ˚. Each prototype is the mean vector of the embedded support points belonging to its class: c k= 1 jS kj X (x i;y i)2S k f ˚(x i) (1) Given a distance function d: R M R 
Inductive Representation Learning on Large Graphs
proceedings.neurips.ccnode classification, clustering, and link prediction [11, 28, 35]. ... (e.g., citation data with text attributes, biological data with functional/molecular markers), our approach can also make use of structural features that are present in all graphs (e.g., node degrees). ... through theoretical analysis, that GraphSAGE is capable of learning ...
Bootstrap Your Own Latent A New Approach to Self ...
proceedings.neurips.ccmining strategies [14, 15] to retrieve the nega-tive pairs. In addition, their performance criti-cally depends on the choice of image augmenta- ... to prevent collapsing while preserving high performance. To prevent collapse, a straightforward solution …
Spatial Transformer Networks - NeurIPS
proceedings.neurips.ccConvolutional Neural Networks define an exceptionally powerful class of models, ... localisation, semantic segmentation, and action recognition tasks, amongst others. ... can take any form, such as a fully-connected network or a convolutional network, but should include a final regression layer to produce the transformation ...
Semi-supervised Learning with Deep Generative Models
proceedings.neurips.ccapproximately invariant to local perturbations along the manifold. The idea of manifold learning ... We show for the first time how variational inference can be brought to bear upon the prob- ... probabilities are formed by a non-linear transformation, with parameters , of a set of latent vari-ables z. This non-linear transformation is ...
Unsupervised Learning of Visual Features by Contrasting ...
proceedings.neurips.ccpseudo-labels to learn visual representations. This method scales to large uncurated dataset and can be used for pre-training of supervised networks [7]. However, their formulation is not principled and recently, Asano et al. [2] show how to cast the pseudo-label assignment problem as an instance of the optimal transport problem.
PyTorch: An Imperative Style, High-Performance Deep ...
proceedings.neurips.ccFacebook AI Research benoitsteiner@fb.com Lu Fang Facebook lufang@fb.com Junjie Bai Facebook jbai@fb.com Soumith Chintala Facebook AI Research soumith@gmail.com Abstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals
Visualizing the Loss Landscape of Neural Nets
proceedings.neurips.cctask that is hard in theory, but sometimes easy in practice. Despite the NP-hardness of training general neural loss functions [3], simple gradient methods often find global minimizers (parameter configurations with zero or near-zero training loss), even when data and labels are randomized before training [43].
InfoGAN: Interpretable Representation Learning by ...
proceedings.neurips.ccof the digit (0-9), and chose to have two additional continuous variables that represent the digit’s angle and thickness of the digit’s stroke. It would be useful if we could recover these concepts without any supervision, by simply specifying that an MNIST digit is generated by an 1-of-10 variable and two continuous variables.
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Lidar 101: An Introduction to Lidar Technology, Data, and ...
coast.noaa.govLight detection and ranging (lidar) mapping is an accepted method of generating precise and directly georeferenced spatial information about the shape and surface characteristics of the Earth. Recent advancements in lidar mapping systems and their enabling technologies allow
World s smallest Time-of-Flight ranging and gesture ...
www.st.comWorld’s smallest Time-of-Flight ranging and gesture detection sensor Datasheet -production data Features Fully integrated miniature module – 940 nm laser VCSEL – VCSEL driver – Ranging sensor with advanced embedded micro controller – 4.4 x 2.4 x 1.0 mm Fast, accurate distance ranging – Measures absolute range up to 2 m
Datasheet - VL53L3CX - Time-of-Flight ranging sensor with ...
www.st.comTime-of-Flight ranging sensor with multi target detection VL53L3CX Datasheet DS13204 - Rev 4 - October 2021 For further information contact your local STMicroelectronics sales office. www.st.com. Description The VL53L3CX is the latest Time-of-Flight (ToF) product from STMicroelectronics
Limit of Blank (LOB), Limit of Detection (LOD), and Limit ...
juniperpublishers.comlimit of detection and limit of quantitation are produced. Signal to Noise Ratio (S/N) is a dimensionless measure of the comparatively strength of an analytical signal (S) to the median strength of the background instrumental noise (N) for a particular sample and is closely related to the detection level.
Antigen-detection in the diagnosis of SARS -CoV-2 ...
apps.who.intAntigen-detection in the diagnosis of SARS-CoV-2 infection using rapid immunoassays: Interim guidance -2- and cohorting of the most infectious cases and their close contacts (16). Patients who present more than 5-7 days after the onset of symptoms are more likely to have lower viral loads, and the likelihood of false negative results