Things to Do in L.A. Tap to enable a layout that focuses on the article. Bernie Sanders kicks off billionaires tax campaign with choice words for the'oligarchs' Sen. Bernie Sanders speaks Wednesday night at the Wiltern at the formal kickoff of the campaign for the California billionaires tax. This is read by an automated voice. Please report any issues or inconsistencies here . Populist Sen. Bernie Sanders on Wednesday formally kicked off the campaign to place a billionaires tax on the November ballot, framing the proposal as something larger than a debate about economic and tax policy as he appeared at a storied Los Angeles venue.

Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.

Figure 1 shows the error analysis for three object detectors, namely, Faster-RCNN, YOLOv5, and DeTR.

These works are regarded as passive works for object detection as they take the input image as is. However, convergence to global minima is not guaranteed to be optimal in neural networks; therefore, we argue that the trained weights in the object detector are not optimal. To rectify this problem, we propose a wrapper based on proactive schemes, PrObeD, which enhances the performance of these object detectors by learning a signal. PrObeD consists of an encoder-decoder architecture, where the encoder network generates an image-dependent signal termed templates to encrypt the input images, and the decoder recovers this template from the encrypted images. We propose that learning the optimum template results in an object detector with an improved detection performance. The template acts as a mask to the input images to highlight semantics useful for the object detector. Finetuning the object detector with these encrypted images enhances the detection performance for both generic and camouflaged.

These techniques calibrate an initial non-metric distance matrix estimated on incomplete data to a distance metric.

The zero-shot open-vocabulary setting poses challenges for conventional image classification.

A probabilistic classifier is considered "well calibrated" when its predicted probabilities closely align with the empirical frequencies of the corresponding labels [