Neural Information Processing Systems http://nips.cc/

However, it is unrealistic to label all the object classes, thus making these supervised learning methods struggle to recognize objects which are unseen during training.

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently.

Proof of Lemma 2. Identifying the second and first order terms in X we get: The last equality allows to conclude the proof. Additionally, assume that either Assumption 2 or Assumption 3 holds. This concludes the proof according to Lemma 1. Here we establish an auxiliary result, controlling the convergence of Neumann iterates to the matrix inverse. Note that Proposition A.1 can easily be extended to the general case by working with M (61) i.e., a M nonlinearity is applied to the activations.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors present a novel approach to learning to rank. In contrast to traditional approaches, the idea is to focus on the number of positive instances that are ranked before the first negative one. Following a large-margin approach leads to primal and dual representations. Compared to similar approaches, the complexity is only linear in the number of instances.

We propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Graph-based algorithms are among the most successful paradigms for solving semi-supervised learning tasks.

For this problem, we propose a novel generalization of random walk, diffusion, or smooth function methods in the literature to a convex p-norm cut function.