The summary of the datasets are listed in Table 1.

While it is expected to understand their intrinsic decision-making processes, these deep neural networks often work in a black-box way.

A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand -- despite the occlusion induced by hand-object interactions. Based on the observation that hand appearance is a strong indicator of the location and size of the active object, we set up our active object detection method as a sequential decision-making process that is conditioned on the location and appearance of the hands. The key innovation of our approach is the design of the active object detection policy that uses an internal representation called the Relational Box Field, which allows for every pixel to regress an improved location of an active object bounding box, essentially giving every pixel the ability to vote for a better bounding box location. The policy is trained using a hybrid imitation learning and reinforcement learning approach, and at test time, the policy is used repeatedly to refine the bounding box location of the active object. We perform experiments on two large-scale datasets: 100DOH and MECCANO, improving AP50 performance by 8% and 30%, respectively, over the state of the art.

In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfolio selection strategies that optimize the growth rate of the capital. We investigate the effect of algorithm parameter m: the number of selected stocks on achieved average annual yields. Results suggest the algorithm's practical usefulness in everyday trading.

In this paper we analyze judgement aggregation problems in which a group of agents independently votes on a set of complex propositions that has some interdependency constraint between them(e.g., transitivity when describing preferences). We consider the issue of judgement aggregation from the perspective of approximation. That is, we generalize the previous results by studying approximate judgement aggregation. We relax the main two constraints assumed in the current literature, Consistency and Independence and consider mechanisms that only approximately satisfy these constraints, that is, satisfy them up to a small portion of the inputs. The main question we raise is whether the relaxation of these notions significantly alters the class of satisfying aggregation mechanisms. The recent works for preference aggregation of Kalai, Mossel, and Keller fit into this framework. The main result of this paper is that, as in the case of preference aggregation, in the case of a subclass of a natural class of aggregation problems termed `truth-functional agendas', the set of satisfying aggregation mechanisms does not extend non-trivially when relaxing the constraints. Our proof techniques involve Boolean Fourier transform and analysis of voter influences for voting protocols. The question we raise for Approximate Aggregation can be stated in terms of Property Testing. For instance, as a corollary from our result we get a generalization of the classic result for property testing of linearity of Boolean functions. An updated version (RePEc:huj:dispap:dp574R) is available at http://www.ratio.huji.ac.il/dp_files/dp574R.pdf