Articulation-centric 2D/3D pose supervision forms the core training objective in most existing 3D human pose estimation techniques. Except for synthetic source environments, acquiring such rich supervision for each real target domain at deployment is highly inconvenient. However, we realize that standard foreground silhouette estimation techniques (on static camera feeds) remain unaffected by domain-shifts. Motivated by this, we propose a novel target adaptation framework that relies only on silhouette supervision to adapt a source-trained model-based regressor. However, in the absence of any auxiliary cue (multi-view, depth, or 2D pose), an isolated silhouette loss fails to provide a reliable pose-specific gradient and requires to be employed in tandem with a topology-centric loss. To this end, we develop a series of convolution-friendly spatial transformations in order to disentangle a topological-skeleton representation from the raw silhouette. Such a design paves the way to devise a Chamfer-inspired spatial topological-alignment loss via distance field computation, while effectively avoiding any gradient hindering spatial-to-pointset mapping. Experimental results demonstrate our superiority against prior-arts in self-adapting a source trained model to diverse unlabeled target domains, such as a) in-the-wild datasets, b) low-resolution image domains, and c) adversarially perturbed image domains (via UAP).

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper considers transfer learning in a multi-armed bandit setting. The model considered has a sequence of episodes, and in each episode, the vector of distributions (one for each arm) is drawn iid from a discrete distribution. In this setting, it is possible to exploit history to learn what this discrete distribution is, and to use this information to reduce regret in each episode. An algorithm is proposed that does this, and cumulative regret bounds are shown for this algorithm.

--Stochastic differential equations such as the Ornstein-Uhlenbeck process have long been used to model real-world probablistic events such as stock prices and temperature fluctuations. While statistical methods such as Maximum Likelihood Estimation (MLE), Kalman Filtering, Inverse V ariable Method, and more have historically been used to estimate the parameters of stochastic differential equations, the recent explosion of deep learning technology suggests that models such as a Recurrent Neural Network (RNN) could produce more precise estimators. We present a series of experiments that compare the estimation accuracy and computational expensiveness of a statistical method (MLE) with a deep learning model (RNN) for the parameters of the Ornstein-Uhlenbeck process. I NTRODUCTION In section I, we will define the Ornstein-Uhlenbeck (OU) stochastic process and explore some of the theory behind its solution. After introducing useful properties of the OU process, we can define the likelihood function to optimize for MLE estimation and search algorithm(s) we will use to obtain estimates.

Articulation-centric 2D/3D pose supervision forms the core training objective in most existing 3D human pose estimation techniques. Except for synthetic source environments, acquiring such rich supervision for each real target domain at deployment is highly inconvenient. However, we realize that standard foreground silhouette estimation techniques (on static camera feeds) remain unaffected by domain-shifts. Motivated by this, we propose a novel target adaptation framework that relies only on silhouette supervision to adapt a source-trained model-based regressor. However, in the absence of any auxiliary cue (multi-view, depth, or 2D pose), an isolated silhouette loss fails to provide a reliable pose-specific gradient and requires to be employed in tandem with a topology-centric loss.

Ensuring the reliability and validity of data-driven quadrotor model predictions is essential for their accepted and practical use. This is especially true for grey- and black-box models wherein the mapping of inputs to predictions is not transparent and subsequent reliability notoriously difficult to ascertain. Nonetheless, such techniques are frequently and successfully used to identify quadrotor models. Prediction intervals (PIs) may be employed to provide insight into the consistency and accuracy of model predictions. This paper estimates such PIs for polynomial and Artificial Neural Network (ANN) quadrotor aerodynamic models. Two existing ANN PI estimation techniques - the bootstrap method and the quality driven method - are validated numerically for quadrotor aerodynamic models using an existing high-fidelity quadrotor simulation. Quadrotor aerodynamic models are then identified on real quadrotor flight data to demonstrate their utility and explore their sensitivity to model interpolation and extrapolation. It is found that the ANN-based PIs widen considerably when extrapolating and remain constant, or shrink, when interpolating. While this behaviour also occurs for the polynomial PIs, it is of lower magnitude. The estimated PIs establish probabilistic bounds within which the quadrotor model outputs will likely lie, subject to modelling and measurement uncertainties that are reflected through the PI widths.

One of the preeminent obstacles to scaling multi-agent reinforcement learning to large numbers of agents is assigning credit to individual agents' actions. In this paper, we address this credit assignment problem with an approach that we call \textit{partial reward decoupling} (PRD), which attempts to decompose large cooperative multi-agent RL problems into decoupled subproblems involving subsets of agents, thereby simplifying credit assignment. We empirically demonstrate that decomposing the RL problem using PRD in an actor-critic algorithm results in lower variance policy gradient estimates, which improves data efficiency, learning stability, and asymptotic performance across a wide array of multi-agent RL tasks, compared to various other actor-critic approaches. Additionally, we relate our approach to counterfactual multi-agent policy gradient (COMA), a state-of-the-art MARL algorithm, and empirically show that our approach outperforms COMA by making better use of information in agents' reward streams, and by enabling recent advances in advantage estimation to be used.

We formally introduce a time series statistical learning method, called Adaptive Learning, capable of handling model selection, out-of-sample forecasting and interpretation in a noisy environment. Through simulation studies we demonstrate that the method can outperform traditional model selection techniques such as AIC and BIC in the presence of regime-switching, as well as facilitating window size determination when the Data Generating Process is time-varying. Empirically, we use the method to forecast S&P 500 returns across multiple forecast horizons, employing information from the VIX Curve and the Yield Curve. We find that Adaptive Learning models are generally on par with, if not better than, the best of the parametric models a posteriori, evaluated in terms of MSE, while also outperforming under cross validation. We present a financial application of the learning results and an interpretation of the learning regime during the 2020 market crash. These studies can be extended in both a statistical direction and in terms of financial applications.

In addition, recent years witnessed significant progress in virtual reality, augmented reality, and smart wearable devices, creating challenges in deploying deep learning systems to portable devices with limited resources (e.g. Now let's take a look at a few papers that introduced novel compression models: In this paper, the authors propose two novel network quantization approaches single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ). The network quantization is considered from both width and depth level. In this paper the authors proposed an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, this method considers the whole network to choose the right rank configuration. It combines three techniques -- value quantization with sparsity multiplication, base encoding, and zero-run encoding.

The recent success of neural networks in applications such as speech recognition, vision and autonomous navigation has led to great excitement by members of the artificial intelligence (AI) community and the general public at large. Over a relatively short period, by the science clock, we managed to automate some tasks that have defied us for decades and using one of the more classical techniques coming out of artificial intelligence research. The triumph over these achievements has led some to describe the automation of these tasks as having reached human level intelligence. This perception, originally hinted at in academic circles, has been gaining momentum more broadly and is leading to some implications. For example, a trend is emerging in which machine learning research is being streamlined into neural network research, under its newly acquired label "deep learning." This perception has also caused some to question the wisdom of continuing to invest in other machine learning approaches, or even mainstream areas of artificial intelligence, such as knowledge representation, symbolic reasoning and planning. Some coverage of AI in public arenas, particularly comments made by some visible figures, has led to mixing this excitement with fear of what AI may be bringing us in the future (i.e., doomsday scenarios).