computer-sciencemathalgorithmsartificial-intelligenceconvex-hulln-queens-problemhill-climbing-algorithm UpdatedApr 12, 2022 Java convex-hullquick-hullgraham-scan UpdatedFeb 23, 2018 Java A tool to create the convex hull of a set of points using the Graham scan algorithm. ...
most of the state-of-the-art techniques of few-shot learning employ transfer learning, which still requires massive labeled data to train. To simulate the human learning mechanism, a deep model of few-shot learning is proposed to learn from one, ...
We investigate the problem of online convex optimization with unknown delays, in which the feedback of a decision arrives with an arbitrary delay. Previous studies have presented delayed online gradient descent (DOGD), and achieved the regret bound ofby only utilizing the convexity condition, where...
Although stochastic gradient descent (SGD) method and its variants (e.g., stochastic momentum methods, AdaGrad) are the choice of algorithms for solving non-convex problems (especially deep learning), there still remain big gaps between the theory and the practice with many questions unresolved. ...
已在人工智能、应用数学领域TOP期刊如IEEE Transactions on Pattern Analysis and Machine Intelligence(5), IEEE Transactions on Information Theory,IEEE Transactions on Image Processing(2),IEEE Transactions on Geoscience and Remote Sensi...
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We propose two new algorithms for the sparse reinforcement learning problem based on different formulations. The first algorithm is an off-line method based on the alternating direction method of multipliers for solving a constrained formulation that explicitly controls the projected Bellman residual. The...
()cvxpylayer=CvxpyLayer(problem,parameters=[A,b],variables=[x])A_tch=torch.randn(m,n,requires_grad=True)b_tch=torch.randn(m,requires_grad=True)# solve the problemsolution,=cvxpylayer(A_tch,b_tch)# compute the gradient of the sum of the solution with respect to A, bsolution.sum()....
However, convexity more closely resembles the intensity deltas needed to push reinforcement learning agent to take greater notice of small advances beyond the low-hanging fruit of its earliest findings, to counteract the naturally concave, diminishing returns that natural optimization problems tend to ha...
but determining if that same problem is a DCP is straightforward. A number of common numerical methods for optimization can be adapted to solve DCPs. The conversion of DCPs to solvable form can be fully automated, and the natural problem structure in DCPs can be exploited to improve performance...