Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner. However, the single-layer architecture commonly used in current SIVI methods can be insufficient when the target posterior has complicated structures. In this paper, we propose hierarchical semi-implicit variational inference, called HSIVI, which generalizes SIVI to allow more expressive multi-layer construction of semi-implicit distributions. By introducing auxiliary distributions that interpolate between a simple base distribution and the target distribution, the conditional layers can be trained by progressively matching these auxiliary distributions one layer after another. Moreover, given pre-trained score networks, HSIVI can be used to accelerate the sampling process of diffusion models with the score matching objective. We show that HSIVI significantly enhances the expressiveness of SIVI on several Bayesian inference problems with complicated target distributions. When used for diffusion model acceleration, we show that HSIVI can produce high quality samples comparable to or better than the existing fast diffusion model based samplers with a small number of function evaluations on various datasets.

Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appearance aggregation. Due to potentially diverse object appearances, the existing approaches often lead to overgeneralization and false positive detections. Furthermore, the best-performing methods train object localization by a surrogate loss, that predicts a unit Gaussian at each object center. This loss is sensitive to annotation error, hyperparameters and does not directly optimize the detection task, leading to suboptimal counts.

As societal awareness of climate change grows, corporate climate policy engagements are attracting attention. We propose a dataset to estimate corporate climate policy engagement from various PDF-formatted documents. Our dataset comes from LobbyMap (a platform operated by global think tank InfluenceMap) that provides engagement categories and stances on the documents. To convert the LobbyMap data into the structured dataset, we developed a pipeline using text extraction and OCR. Our contributions are: (i) Building an NLP dataset including 10K documents on corporate climate policy engagement.

Global flash floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand underscore the urgent need for precise flood mapping to guide restoration efforts, understand vulnerabilities, and prepare for future occurrences. While Synthetic Aperture Radar (SAR) remote sensing offers day-and-night, all-weather imaging capabilities, its application in deep learning for flood segmentation is limited by the lack of large annotated datasets. To address this, we introduce Kuro Siwo, a manually annotated multi-temporal dataset, spanning 43 flood events globally.

PongBot is an artificial intelligence-powered tennis robot. American tennis player Danielle Collins had some choice words for the cameraman during her Internationaux de Strasbourg match against Emma Raducanu on Wednesday afternoon. Collins was in the middle of a changeover when she felt the cameraman's hovering was a bit too close for comfort in the middle of the third and defining set. She got off the bench and made the point clear. Danielle Collins celebrates during her match against Madison Keys in the third round of the women's singles at the 2025 Australian Open at Melbourne Park in Melbourne, Australia, on Jan. 18, 2025.

In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of predictions to all norm-bounded attacks is achieved through randomised smoothing of network inputs. Today's state-of-the-art certifications make optimal use of the class output scores at the input instance under test: no better radius of certification (under the L

We conduct further ablation studies for our approach in this supplementary material, including comparison with test-time optimization and sensitivity analysis of the refinement stage. Additional qualitative results are also provided. We compare our coarse-to-fine approach with the testtime optimization approach. As has been done in our coarse-to-fine pipeline, we also use the output from our coarse estimation stage as an initialization. Instead of apply the mesh refinement GCN, we further optimize the SMAL parameters based on the keypoints and silhouettes for 10, 50, 100, 200 iterations, respectively.

Supplementary Material for the Paper entitled "ABC: Auxiliary Balanced Classifier for Class-Imbalanced Semi-Supervised Learning" Figure 1 illustrates the overall procedure of consistency regularization for the ABC. Detailed procedure is described in Section 3.4 of the main paper. The pseudo code of the proposed algorithm is presented in Algorithm 1. The for loop (lines 2 14) can be run in parallel. Two types of class imbalance for the considered datasets are illustrated in Figure 2. In Figure 2 (b), we can see that each minority class has a very small amount of data. Existing SSL algorithms can be significantly biased toward majority classes under step imbalanced settings.

Robots zipping down footpaths may sound futuristic, but they are increasingly being put to work making deliveries around the world – though a legal minefield and cautious approach to new tech means they are largely absent in Australia. Retail and food businesses have been using robots for a variety of reasons, with hazard detection robots popping up in certain Woolworths stores and virtual waiters taking dishes from kitchens in understaffed restaurants to hungry diners in recent years. Overseas, in jurisdictions such as California, robots are far more visible in everyday life. Following on from the first wave of self-driving car trials in cities such as San Francisco, humans now also share footpaths with robots. Likened to lockers on wheels, companies including Serve Robotics and Coco have partnered with Uber Eats and Doordash, which have armies of robots travelling along footpaths in Los Angeles delivering takeaway meals and groceries.

Over the last decade, Dean has amassed a healthy collection of video games, from smash hits to cult classics. His digital library is like a modern day Blockbuster, all readily accessible with just a click or two. But his son, Sam, has eyes for only one video game: Roblox, the behemoth virtual universe-slash-video game that's among the most popular on the planet. The company reports that more than 97 million people log on to Roblox every day. Around 40% of those are, like Sam, under 13 years of age.