We appreciate the reviewers for the constructive comments on this paper. One common concern is our baseline for RetinaNet/Mask-RCNN is not strong. ImageNet Pre-Training." is comparable with our baseline (39.5% vs 39.24%). R1Q1: How do the latencies change on GPU? R1Q2: The improvements are not large. R1Q3: Compare to prior BSS search methods like POP[22] in Table 1.

The lifespan of a games console has extended a lot since I was a child. In the 1990s, this kind of technology would be out of date after just a couple of years. There would be some tantalising new machine out before you knew it, everybody competing to be on the cutting edge: the Game Boy and Sega Genesis/Mega Drive in 1989 were followed by the Game Gear in 1990 and the Super NES in 1991. Five years was a long life for a gaming machine. The Nintendo Switch 2 will be released in a couple of weeks, more than eight years since I first picked an original Switch up off its dock and marvelled at the instant transition to portable play.

Breakthroughs, discoveries, and DIY tips sent every weekday. Our planet runs on pollinators. Without bees, moths, weevils, and more zooming around and spreading plants' reproductive cells, plants and important crops would not grow. Without plants we would not breathe or eat. When these crucial pollinating species visit flowers and other plants, they produce a number of characteristic sounds, such as wing flapping when hovering, landing, and taking off.

Google unveiled a slew of new AI tools and features at I/O, dropping the term Gemini 95 times and AI 92 times. However, the best announcement of the entire show wasn't an AI feature; rather, the title went to one of the two hardware products announced -- the Android XR glasses. Also: I'm an AI expert, and these 8 announcements at Google I/O impressed me the most For the first time, Google gave the public a look at its long-awaited smart glasses, which pack Gemini's assistance, in-lens displays, speakers, cameras, and mics into the form factor of traditional eyeglasses. I had the opportunity to wear them for five minutes, during which I ran through a demo of using them to get visual Gemini assistance, take photos, and get navigation directions. As a Meta Ray-Bans user, I couldn't help but notice the similarities and differences between the two smart glasses -- and the features I now wish my Meta pair had.

Below we provide further examples of specifications that can be captured by our framework. Another desirable specification towards ensuring reliable uncertainty calibration for NNs is that the expected uncertainty in the predictions increases monotonically with an increase in the variance of the input-noise distribution. We can capture this specification within the formulation described by equation 1, by letting: 1. P A natural generalization of this specification is one where low reconstruction error is guaranteed in expectation, since in practice the latent-representations that are fed into the decoder are drawn from a normal distribution whose mean and variance are predicted by the encoder. A more general specification is one where we wish to verify that for a set of norm-bounded points around a given input, the expected reconstruction error from the VAE is small. Writing this in terms of expected values, we obtain g (λ).

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i.e., cyclic sequences). We train DACT using Proximal Policy Optimization and design a curriculum learning strategy for better sample efficiency. We apply DACT to solve the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Results show that our DACT outperforms existing Transformer based improvement models, and exhibits much better generalization performance across different problem sizes on synthetic and benchmark instances, respectively.

Datasets: Three real-world datasets (MNIST, Tiny-ImageNet, CIFAR-10) are used to evaluate the proposed model.

We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors.

State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data. State-of-the-art probabilistic approaches are often able to scale to large problems at the cost of flexibility of the variational posterior or expressivity of the dynamics model. However, those consolidations can be detrimental if the ultimate goal is to learn a generative model capable of explaining the spatiotemporal structure of the data and making accurate forecasts. We introduce a low-rank structured variational autoencoding framework for nonlinear Gaussian state-space graphical models capable of capturing dense covariance structures that are important for learning dynamical systems with predictive capabilities. Our inference algorithm exploits the covariance structures that arise naturally from sample based approximate Gaussian message passing and low-rank amortized posterior updates - effectively performing approximate variational smoothing with time complexity scaling linearly in the state dimensionality. In comparisons with other deep state-space model architectures our approach consistently demonstrates the ability to learn a more predictive generative model. Furthermore, when applied to neural physiological recordings, our approach is able to learn a dynamical system capable of forecasting population spiking and behavioral correlates from a small portion of single trials.

Microsoft has begun testing a new power-saving technology within Windows, as well as assigning AI actions to a right-click menu within File Explorer. Microsoft is also tweaking the way in which widgets are laid out, letting Copilot handle the decisions itself. Microsoft published the changes as part of the Windows 11 Insider Preview Build 26120.4151 By testing these features, Microsoft doesn't necessarily have to commit to eventually rolling them out, although many appear to be under consideration for a more general release. Under the hood, Microsoft said that it's testing out what it calls User Interaction-Aware CPU Power Management, "an OS-level enhancement that helps reduce power consumption and extend your battery life."