Incomplete multi-view clustering aims to learn complete correlations among samples by leveraging complementary information across multiple views for clustering. Anchor-based methods further establish sample-level similarities for representative anchor generation, effectively addressing scalability issues in large-scale scenarios. Despite efficiency improvements, existing methods overlook the misguidance in anchors learning induced by partial missing samples, i.e., the absence of samples results in shift of learned anchors, further leading to sub-optimal clustering performance. To conquer the challenges, our solution involves a cross-view reconstruction strategy that not only alleviate the anchor shift problem through a carefully designed cross-view learning process, but also reconstructs missing samples in a way that transcends the limitations imposed by convex combinations. By employing affine combinations, our method explores areas beyond the convex hull defined by anchors, thereby illuminating blind spots in the reconstruction of missing samples. Experimental results on four benchmark datasets and three large-scale datasets validate the effectiveness of our proposed method.

Recent methods are proposed to improve performance of domain adaptation by inferring domain index under an adversarial variational bayesian framework, where domain index is unavailable. However, existing methods typically assume that the global domain indices are sampled from a vanilla gaussian prior, overlooking the inherent structures among different domains. To address this challenge, we propose a Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing(GMDI) algorithm. GMDI employs a Gaussian Mixture Model for domain indices, with the number of component distributions in the "domain-themes" space adaptively determined by a Chinese Restaurant Process. By dynamically adjusting the mixtures at the domain indices level, GMDI significantly improves domain adaptation performance. Our theoretical analysis demonstrates that GMDI achieves a more stringent evidence lower bound, closer to the log-likelihood. For classification, GMDI outperforms all approaches, and surpasses the state-of-the-art method, VDI, by up to 3.4%, reaching 99.3%. For regression, GMDI reduces MSE by up to 21% (from 3.160 to 2.493), achieving the lowest errors among all methods. Source code is publicly available from https://github.com/lingyf3/GMDI.

It is well known that query-based attacks tend to have relatively higher success rates in adversarial black-box attacks. While research on black-box attacks is actively being conducted, relatively few studies have focused on pixel attacks that target only a limited number of pixels. In image classification, query-based pixel attacks often rely on patches, which heavily depend on randomness and neglect the fact that scattered pixels are more suitable for adversarial attacks. Moreover, to the best of our knowledge, query-based pixel attacks have not been explored in the field of object detection. To address these issues, we propose a novel pixel-based black-box attack called Remember and Forget Pixel Attack using Reinforcement Learning(RFPAR), consisting of two main components: the Remember and Forget processes.

Exploiting symmetry inherent in data can significantly improve the sample efficiency of a learning procedure and the generalization of learned models. When data clearly reveals underlying symmetry, leveraging this symmetry can naturally inform the design of model architectures or learning strategies. Yet, in numerous real-world scenarios, identifying the specific symmetry within a given data distribution often proves ambiguous. To tackle this, some existing works learn symmetry in a data-driven manner, parameterizing and learning expected symmetry through data. However, these methods often rely on explicit knowledge, such as pre-defined Lie groups, which are typically restricted to linear or affine transformations.

Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across dispersed nodes without having to force individual nodes to share data. However, its broad adoption is hindered by the high communication costs of transmitting a large number of model parameters. This paper presents EvoFed, a novel approach that integrates Evolutionary Strategies (ES) with FL to address these challenges. EvoFed employs a concept of'fitness-based information sharing', deviating significantly from the conventional model-based FL. Rather than exchanging the actual updated model parameters, each node transmits a distance-based similarity measure between the locally updated model and each member of the noise-perturbed model population.

AI chatbots are stepping into the therapist's chair – and not everyone is thrilled about it. In March alone, 16.7 million posts from TikTok users discussed using ChatGPT as a therapist, but mental health professionals are raising red flags over the growing trend that sees artificial intelligence tools being used in their place to treat anxiety, depression and other mental health challenges. "ChatGPT singlehandedly has made me a less anxious person when it comes to dating, when it comes to health, when it comes to career," user @christinazozulya shared in a TikTok video posted to her profile last month. "Any time I have anxiety, instead of bombarding my parents with texts like I used to or texting a friend or crashing out essentially… before doing that, I always voice memo my thoughts into ChatGPT, and it does a really good job at calming me down and providing me with that immediate relief that unfortunately isn't as accessible to everyone." The ChatGPT logo on a laptop computer arranged in New York, US, on Thursday, March 9, 2023.

Putting on Project Moohan, an upcoming XR headset developed by Google, Samsung, and Qualcomm, for the first time felt strangely familiar. From twisting the head-strap knob on the back to slipping the standalone battery pack into my pants pocket, my mind was transported back to February 2024, when I tried on the Apple Vision Pro on launch day. Also: I tried Google's XR glasses and they already beat my Meta Ray-Bans in 3 ways Only this time, the headset was powered by Android XR, Google's newest operating system built around Gemini, the same AI model that dominated the Google I/O headlines throughout this week. The difference in software was immediately noticeable -- from the home grid of Google apps like Photos, Maps, and YouTube (which VisionOS still lacks) to prompting for Gemini instead of Siri with a long press of the headset's multifunctional key. While my demo with Project Moohan lasted only about 10 minutes, it gave me a clear understanding of how it's challenging Apple's Vision Pro and how Google, Samsung, and Qualcomm plan to convince the masses that the future of spatial computing does, in fact, live in a bulkier space-helmet-like device.

If there's one thing that turns people off from adopting new tech, it's being forced to look silly and feel uncomfortable for extended lengths of time. It was always the Achilles' heel for 3D in the past, and it remains the primary hurdle for VR headsets and goofy-looking smart glasses. Laptops, tablets, and even computer monitors have started embracing a new form of 3D technology that solves this problem entirely, without giving up just how compelling 3D can look. I've used the latest iteration of the technology and spoke with the creators--this might finally be the version of 3D that sticks. I was skeptical when I first saw this next generation of 3D technology. Interest in 3D comes in waves.

Multiple clustering aims to discover various latent structures of data from different aspects. Deep multiple clustering methods have achieved remarkable performance by exploiting complex patterns and relationships in data. However, existing works struggle to flexibly adapt to diverse user-specific needs in data grouping, which may require manual understanding of each clustering. To address these limitations, we introduce Multi-Sub, a novel end-to-end multiple clustering approach that incorporates a multi-modal subspace proxy learning framework in this work. Utilizing the synergistic capabilities of CLIP and GPT-4, Multi-Sub aligns textual prompts expressing user preferences with their corresponding visual representations. This is achieved by automatically generating proxy words from large language models that act as subspace bases, thus allowing for the customized representation of data in terms specific to the user's interests. Our method consistently outperforms existing baselines across a broad set of datasets in visual multiple clustering tasks.

We study theoretical properties of a broad class of regularized algorithms with vector-valued output. These spectral algorithms include kernel ridge regression, kernel principal component regression and various implementations of gradient descent.