Baselines We used the ERM, LDAM 1, Focal 2, IW 3, and VAT 4 baseline implementations.

Now we look into the term[(A+I)X]TV,:, which is the aggregated feature vectors within neighborhood N1 for nodes in the training set. Note that [(A + I)X]TS,: is a circulant matrix, therefore its inverse exists. Now consider an arbitrary training datapoint(v,yv) TV, and a perturbation added to the neighborhood N(v) of node v, such that the number of nodes with a randomly selected class labelyp Y 6=yv isδ1lessthanexpectedin N(v). Now we move on to discuss the GCN layer formulated asf(X;A,W) = AXW without self loops. We regardcs,i as the coefficient ofs at frequency componenti and regard the coefficients at all frequencies components{cs,i} as the spectrum of signalswith respect to graphG.

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.

Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data and is often used to identify the top-k promising policies for deployment in online A/B tests. Existing evaluation metrics for OPE estimators primarily focus on the "accuracy" of OPE or that of downstream policy selection, neglecting risk-return tradeoff in the subsequent online policy deployment. To address this issue, we draw inspiration from portfolio evaluation in finance and develop a new metric, called SharpeRatio@k, which measures the risk-return tradeoff of policy portfolios formed by an OPE estimator under varying online evaluation budgets (k). We validate our metric in two example scenarios, demonstrating its ability to effectively distinguish between low-risk and high-risk estimators and to accurately identify the most efficient estimator. This efficient estimator is characterized by its capability to form the most advantageous policy portfolios, maximizing returns while minimizing risks during online deployment, a nuance that existing metrics typically overlook. These experiments offer several interesting directions and suggestions for future OPE research. Reinforcement Learning (RL) has achieved considerable success in a variety of applications requiring sequential decision-making. Nonetheless, its online learning approach is often seen as problematic due to the need for active interaction with the environment, which can be risky, time-consuming, and unethical (Fu et al., 2021; Matsushima et al., 2021). To mitigate these issues, learning new policies offline from existing historical data, known as Offline RL (Levine et al., 2020), is becoming increasingly popular for real-world applications (Qin et al., 2021). Typically, in the offline RL lifecycle, promising candidate policies are initially screened through Off-Policy Evaluation (OPE) (Fu et al., 2020), followed by the selection of the final production policy from the shortlisted candidates using more dependable online A/B tests (Kurenkov & Kolesnikov, 2022), as shown in Figure 1. When evaluating the efficacy of OPE methods, research has largely concentrated on "accuracy" metrics like mean-squared error (MSE) (Uehara et al., 2022; Voloshin et al., 2019), rank correlation (rankcorr) (Fu et al., 2021; Paine et al., 2020), and regret in subsequent policy selection (Doroudi et al., 2017; Tang & Wiens, 2021). However, these existing metrics do not adequately assess the balance between risk and return experienced by an estimator during the online deployment of selected policies. Crucially, MSE and rankcorr fall short in distinguishing whether an estimator is underevaluating nearoptimal policies or overevaluating poor-performing ones, which influence the risk-return dynamics in OPE and policy selection in different ways.

It is increasingly making it easier for developers to build Machine Learning capabilities into their applications while testing their code is real time. In this piece about Pytorch Tutorial, I talk about the new platform in Deep Learning. The latest version of the platform brings a lot of new capabilities to the table and is clocking vibrant support from the whole industry. It is remarkable how Pytorch is being touted as a serious contender to Google's Tensorflow just within a couple of years of its release. Its popularity is mainly being driven by a smoother learning curve and a cleaner interface, which is providing developers with a more intuitive approach to build neural networks.

Kraken Robotics, Inc. (OTCQB: KRKNF) (TSXV: PNG) is engaged as a marine technology company that develops underwater robotic systems and software-centric sensors. Shares of the underwater robotics company are rallying 35%, through early trading on Wednesday, June 20, 2018. Over the past month, Kraken Robotics, Inc. has seen average daily volume of 78,235 shares. However, volume of 155,625 shares or dollar volume of $24,121, has already exchanged hands on the day. Shares of Kraken Robotics, Inc. are climbing today after the company announced that it has received a non-brokered private placement offering with Ocean Infinity, Ltd., an offshore ocean survey and ocean exploration company.

The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this article, we review our work over the last 10 years in the area of supervised learning, focusing on three interlinked directions of research--(1) theory, (2) engineering applications (making intelligent software), and (3) neuroscience (understanding the brain's mechanisms of learnings)--that contribute to and complement each other. Because seeing is intelligence, learning is also becoming a key to the study of artificial and biological vision. In the last few years, both computer vision--which attempts to build machines that see--and visual neuroscience--which aims to understand how our visual system works--are undergoing a fundamental change in their approaches. Visual neuroscience is beginning to focus on the mechanisms that allow the cortex to adapt its circuitry and learn a new task.

We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army. This technology, which has been in operational use since the summer of 2010, has helped to reduce user work loads by automating repetitive and time-consuming tasks. The technology has also provided the unexpected benefit of enabling standardization of products and processes. Recent technical advances have enabled its use for automating increasingly complex tasks (Allen et al. 2007; Blythe et al. 2008; Burstein et al. 2008; Leshed et al. 2008; Cypher et al. 2010). However, fielded applications of the technology have been limited to macro recording capabilities, which can only reproduce the exact behavior demonstrated by the user.

In this report we motivate research in this area, give an overview of past RTS game AI competitions, and discuss future directions. TS games -- such as StarCraft by Blizzard Entertainment and Command and Conquer by Electronic Arts -- are popular video games that can be described as real-time war simulations in which players delegate units under their command to gather resources, build structures, combat and support units, scout opponent locations, and attack. The winner of an RTS game usually is the player or team that destroys the opponents' structures first. Unlike abstract board games like chess and go, moves in RTS games are executed simultaneously at a rate of at least eight frames per second. In addition, individual moves in RTS games can consist of issuing simultaneous orders to hundreds of units at any given time.

We report on the progress toward the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.