AI的一个关键挑战是构建体现的系统，该系统在动态变化的环境中运行。此类系统必须适应更改任务上下文并持续学习。虽然标准的深度学习系统实现了最先进的静态基准的结果，但它们通常在动态方案中挣扎。在这些设置中，来自多个上下文的错误信号可能会彼此干扰，最终导致称为灾难性遗忘的现象。在本文中，我们将生物学启发的架构调查为对这些问题的解决方案。具体而言，我们表明树突和局部抑制系统的生物物理特性使网络能够以特定于上下文的方式动态限制和路由信息。我们的主要贡献如下。首先，我们提出了一种新颖的人工神经网络架构，该架构将活跃的枝形和稀疏表示融入了标准的深度学习框架中。接下来，我们在需要任务的适应性的两个单独的基准上研究这种架构的性能：Meta-World，一个机器人代理必须学习同时解决各种操纵任务的多任务强化学习环境;和一个持续的学习基准，其中模型的预测任务在整个训练中都会发生变化。对两个基准的分析演示了重叠但不同和稀疏的子网的出现，允许系统流动地使用最小的遗忘。我们的神经实现标志在单一架构上第一次在多任务和持续学习设置上取得了竞争力。我们的研究揭示了神经元的生物学特性如何通知深度学习系统，以解决通常不可能对传统ANN来解决的动态情景。

This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

We study the sample complexity of reducing reinforcement learning to a sequence of empirical risk minimization problems over the policy space. Such reductions-based algorithms exhibit local convergence in the function space, as opposed to the parameter space for policy gradient algorithms, and thus are unaffected by the possibly non-linear or discontinuous parameterization of the policy class. We propose a variance-reduced variant of Conservative Policy Iteration that improves the sample complexity of producing a $\varepsilon$-functional local optimum from $O(\varepsilon^{-4})$ to $O(\varepsilon^{-3})$. Under state-coverage and policy-completeness assumptions, the algorithm enjoys $\varepsilon$-global optimality after sampling $O(\varepsilon^{-2})$ times, improving upon the previously established $O(\varepsilon^{-3})$ sample requirement.

This paper proposes a novel controller framework that provides trajectory tracking for an Aerial Manipulator (AM) while ensuring the safe operation of the system under unknown bounded disturbances. The AM considered here is a 2-DOF (degrees-of-freedom) manipulator rigidly attached to a UAV. Our proposed controller structure follows the conventional inner loop PID control for attitude dynamics and an outer loop controller for tracking a reference trajectory. The outer loop control is based on the Model Predictive Control (MPC) with constraints derived using the Barrier Lyapunov Function (BLF) for the safe operation of the AM. BLF-based constraints are proposed for two objectives, viz. 1) To avoid the AM from colliding with static obstacles like a rectangular wall, and 2) To maintain the end effector of the manipulator within the desired workspace. The proposed BLF ensures that the above-mentioned objectives are satisfied even in the presence of unknown bounded disturbances. The capabilities of the proposed controller are demonstrated through high-fidelity non-linear simulations with parameters derived from a real laboratory scale AM. We compare the performance of our controller with other state-of-the-art MPC controllers for AM.

Virtual Product placement(VPP) is the advertising technique of digitally placing a branded object into the scene of a movie or TV show. This type of advertising provides the ability for brands to reach consumers without interrupting the viewing experience with a commercial break, as the products are seen in the background or as props. Despite this being a billion-dollar industry, ad rendering technique is currently executed at post production stage, manually either with the help of VFx artists or through semi-automated solutions. In this paper, we demonstrate a fully automated framework to digitally place 2-D ads in linear TV cooking shows captured using single-view camera with small camera movements. Without access to full video or production camera configuration, this framework performs the following tasks (i) identifying empty space for 2-D ad placement (ii) kitchen scene understanding (iii) occlusion handling (iv) ambient lighting and (v) ad tracking.

The use of emojis affords a visual modality to, often private, textual communication. The task of predicting emojis however provides a challenge for machine learning as emoji use tends to cluster into the frequently used and the rarely used emojis. Much of the machine learning research on emoji use has focused on high resource languages and has conceptualised the task of predicting emojis around traditional server-side machine learning approaches. However, traditional machine learning approaches for private communication can introduce privacy concerns, as these approaches require all data to be transmitted to a central storage. In this paper, we seek to address the dual concerns of emphasising high resource languages for emoji prediction and risking the privacy of people's data. We introduce a new dataset of $118$k tweets (augmented from $25$k unique tweets) for emoji prediction in Hindi, and propose a modification to the federated learning algorithm, CausalFedGSD, which aims to strike a balance between model performance and user privacy. We show that our approach obtains comparative scores with more complex centralised models while reducing the amount of data required to optimise the models and minimising risks to user privacy.

In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL -- a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment.

我们表明，具有“低稳定器复杂性”的量子状态可以有效地与HAAR随机区分开。具体而言，给定$ n $ qubit的纯状态$ | \ psi \ rangle $，我们给出了一种有效的算法，以区分$ | \ psi \ rangle $是（i）haar-random或（ii）具有稳定器保真度的状态至少$ \ frac {1} {k} $（即，具有一些稳定器状态的保真度至少$ \ frac {1} {k} $），保证就是其中之一。使用Black-box访问$ | \ psi \ rangle $，我们的算法使用$ o \！\ left（k^{12} \ log（1/\ delta）\ right）$ copies $ | \ psi \ rangle $和$ o \！\ left（n k^{12} \ log（1/\ delta）\ right）$ $时间以概率至少$ 1- \ delta $成功，并且随着访问状态准备统一，以$ | | \ psi \ rangle $（及其倒数），$ o \！\ left（k^{3} \ log（1/\ delta）\ right）$ queries和$ o \！\！ log（1/\ delta）\ right）$时间就足够了。作为推论，我们证明$ \ omega（\ log（n））$ $ t $ - 盖特对于任何Clifford+$ t $ circile都是必不可少的，以准备计算上的pseudorandom Quantum Quantum state，这是一种首要的下限。

室内运动计划的重点是解决通过混乱环境导航代理的问题。迄今为止，在该领域已经完成了很多工作，但是这些方法通常无法找到计算廉价的在线路径计划和路径最佳之间的最佳平衡。除此之外，这些作品通常证明是单一启动单目标世界的最佳性。为了应对这些挑战，我们为在未知室内环境中进行导航的多个路径路径计划者和控制器堆栈，在该环境中，路点将目标与机器人必须在达到目标之前必须穿越的中介点一起。我们的方法利用全球规划师（在任何瞬间找到下一个最佳航路点），本地规划师（计划通往特定航路点的路径）以及自适应模型预测性控制策略（用于强大的系统控制和更快的操作） 。我们在一组随机生成的障碍图，中间航路点和起始目标对上评估了算法，结果表明计算成本显着降低，具有高度准确性和可靠的控制。