SAVE 30: As of May 21, the Fire TV Stick 4K Max is on sale for only 29.99 at Amazon with the code MAX4KNEW. Amazon's Fire TV Stick 4K Max can turn your existing TV into artwork for that bougie on a budget aesthetic. As of May 21, the already affordable Fire TV Stick 4K Max is on sale for a very low 29.99 at Amazon when you enter the code MAX4KNEW. The first and only Fire TV Stick with Ambient Experience, the 4K Max lets you choose from over 2,000 works of fine art or use AI art to design your own masterpiece by asking Alexa to generate an original image and choosing a painting style. The only limit is your own imagination. You can also customize your screen with Alexa widgets like calendar, to-dos, weather, and more, and control your compatible smart home devices from your TV.

We thank all reviewers for their careful reviews and many positive comments. We feel that the typos and minor issues are easily addressable and will be corrected. We will incorporate this analysis into a revision of the paper. We thank R1 for bringing this highly related work to our attention. That work focuses on environments for which mice have previously developed spatial maps.

In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient. On-policy methods typically generate reliable policy improvement throughout training, while off-policy methods make more efficient use of data through sample reuse. In this work, we combine the theoretically supported stability benefits of on-policy algorithms with the sample efficiency of off-policy algorithms. We develop policy improvement guarantees that are suitable for the off-policy setting, and connect these bounds to the clipping mechanism used in Proximal Policy Optimization. This motivates an off-policy version of the popular algorithm that we call Generalized Proximal Policy Optimization with Sample Reuse. We demonstrate both theoretically and empirically that our algorithm delivers improved performance by effectively balancing the competing goals of stability and sample efficiency.

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.

Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model. This is because the low-dimensional pose and shape parameters of the SMAL model makes it easier for deep networks to learn the high-dimensional animal meshes. However, the SMAL model is learned from scans of toy animals with limited pose and shape variations, and thus may not be able to represent highly varying real animals well. This may result in poor fittings of the estimated meshes to the 2D evidences, e.g.

Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) introduces the concept of unrolling an iterative algorithm and training it like a neural network. It has had great success on sparse recovery. In this paper, we show that adding momentum to intermediate variables in the LISTA network achieves a better convergence rate and, in particular, the network with instance-optimal parameters is superlinearly convergent. Moreover, our new theoretical results lead to a practical approach of automatically and adaptively calculating the parameters of a LISTA network layer based on its previous layers. Perhaps most surprisingly, such an adaptive-parameter procedure reduces the training of LISTA to tuning only three hyperparameters from data: a new record set in the context of the recent advances on trimming down LISTA complexity. We call this new ultra-light weight network HyperLISTA. Compared to state-of-the-art LISTA models, HyperLISTA achieves almost the same performance on seen data distributions and performs better when tested on unseen distributions (specifically, those with different sparsity levels and nonzero magnitudes).

M-Step only reconstructions with known sources.............. 9 2.5.3 Similar works that use expectation maximization (EM) based deep learning approaches are usually specific to a single task, often times image classification. Results shown are simulated using 20 surface receivers and a varying number of sources (9, 25, and 49) in a uniform grid. The velocity reconstruction MSE is included in the top right of each reconstruction. The model with the highest data likelihood is highlighted in orange.

Unsurprisingly, the bulk of Google's announcements at I/O this week focused on AI. Although past Google I/O events also heavily leaned on AI, what made this year's announcements different is that the features were spread across nearly every Google offering and touched nearly every task people partake in every day. Because I'm an AI optimist, and my job as an AI editor involves testing tools, I have always been pretty open to using AI to optimize my daily tasks. However, Google's keynote made it clear that even those who may not be as open to it will soon find it unavoidable. Moreover, the tech giants' announcements shed light on the industry's future, revealing three major trends about where AI is headed, which you can read more about below.