闭环大脑刺激是指捕获诸如脑电图（EEG）之类的神经生理学措施，迅速识别感兴趣的神经事件，并产生听觉，磁性或电刺激，从而精确地与大脑过程相互作用。这是一种基本神经科学的新方法，也许是临床应用，例如恢复降解记忆功能；但是，现有工具很昂贵，繁琐，并且具有有限的实验灵活性。在本文中，我们提出了Portiloop，这是一种基于深度学习的，便携式和低成本的闭环刺激系统，能够靶向特定的脑振荡。我们首先记录可以从市售组件构建的开放式软件实现。我们还提供了快速，轻巧的神经网络模型和探索算法，该算法自动优化了所需的脑振荡的模型超参数。最后，我们在实时睡眠主轴检测的具有挑战性的测试案例中验证了该技术，结果可与大规模在线数据注释主轴数据集（MODA；组共识）上的离线专家绩效相当。社区可以提供软件和计划，作为开放科学计划，旨在鼓励进一步开发并推动闭环神经科学研究。

飞行机器人通常相当细腻，在面对碰撞的风险时需要保护性围墙，而高复杂性和有效载荷降低是碰撞弹性飞行机器人的反复出现的问题。受节肢动物的外骨骼的启发，我们设计了一个简单，开源的，易于制造的半刚性结构，具有柔软的接头，可以承受高速影响。使用外骨骼，保护壳成为主要机器人结构的一部分，从而最大程度地减少了其有效载荷能力的损失。我们的设计易于使用廉价组件（例如竹串）和消费级3D打印机来构建和自定义。结果是认知，这是一种低于250G的自动脉动四轮摩托车，可在高达7m/s的速度下生存多个碰撞。除了其碰撞弹性外，使用Python或Buzz可以易于编程，还携带传感器，使其可以飞行大约。 17分钟无需GPS或外部运动捕获系统，具有足够的计算能力，可以在板载板上运行深神网络模型，并旨在促进与自动化电池交换系统的集成。通过大大降低破坏自己的硬件或环境的风险，这种结构成为高风险活动（例如在混乱的环境或加固学习培训中飞行）的理想平台。源代码，3D文件，说明和视频可通过项目网站（https://thecognifly.github.io）获得。

This short study reformulates the statistical Bayesian learning problem using a quantum mechanics framework. Density operators representing ensembles of pure states of sample wave functions are used in place probability densities. We show that such representation allows to formulate the statistical Bayesian learning problem in different coordinate systems on the sample space. We further show that such representation allows to learn projections of density operators using a kernel trick. In particular, the study highlights that decomposing wave functions rather than probability densities, as it is done in kernel embedding, allows to preserve the nature of probability operators. Results are illustrated with a simple example using discrete orthogonal wavelet transform of density operators.

Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor long-range dependencies, that can be solved using global attention but significantly increases the computational cost to quadratic complexity. In this work, we propose an alternative approach to overcome these structural limitations by leveraging the ViT/MLP-Mixer architectures introduced in computer vision. We introduce a new class of GNNs, called Graph MLP-Mixer, that holds three key properties. First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on the Long Range Graph Benchmark (LRGB) and the TreeNeighbourMatch datasets. Second, they offer better speed and memory efficiency with a complexity linear to the number of nodes and edges, surpassing the related Graph Transformer and expressive GNN models. Third, they show high expressivity in terms of graph isomorphism as they can distinguish at least 3-WL non-isomorphic graphs. We test our architecture on 4 simulated datasets and 7 real-world benchmarks, and show highly competitive results on all of them.

We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently from many works in approximate recovery, we present results both for bounded Lipschitz losses and for the absolute loss, with the latter relying on Talagrand-type inequalities. The proofs create a bridge between two approaches to the theoretical analysis of matrix completion, since they consist in a combination of techniques from both the exact recovery literature and the approximate recovery literature.

We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a region of interest displaying the object in the image and (ii) a CAD model of the observed object. The contributions of this work are threefold. First, we present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. The shape and coordinate system of the novel object are provided as inputs to the network by rendering multiple synthetic views of the object's CAD model. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner. Third, we introduce a large-scale synthetic dataset of photorealistic images of thousands of objects with diverse visual and shape properties and show that this diversity is crucial to obtain good generalization performance on novel objects. We train our approach on this large synthetic dataset and apply it without retraining to hundreds of novel objects in real images from several pose estimation benchmarks. Our approach achieves state-of-the-art performance on the ModelNet and YCB-Video datasets. An extensive evaluation on the 7 core datasets of the BOP challenge demonstrates that our approach achieves performance competitive with existing approaches that require access to the target objects during training. Code, dataset and trained models are available on the project page: https://megapose6d.github.io/.

When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another. A way to overcome this is to use intrinsic motivation in order to explore new transitions until a reward is found. In this work, we use a recently proposed definition of intrinsic motivation, Curiosity, in an evolutionary policy search method. We propose Curiosity-ES, an evolutionary strategy adapted to use Curiosity as a fitness metric. We compare Curiosity with Novelty, a commonly used diversity metric, and find that Curiosity can generate higher diversity over full episodes without the need for an explicit diversity criterion and lead to multiple policies which find reward.

Deep Neural Networks (DNNs) outshine alternative function approximators in many settings thanks to their modularity in composing any desired differentiable operator. The formed parametrized functional is then tuned to solve a task at hand from simple gradient descent. This modularity comes at the cost of making strict enforcement of constraints on DNNs, e.g. from a priori knowledge of the task, or from desired physical properties, an open challenge. In this paper we propose the first provable affine constraint enforcement method for DNNs that requires minimal changes into a given DNN's forward-pass, that is computationally friendly, and that leaves the optimization of the DNN's parameter to be unconstrained i.e. standard gradient-based method can be employed. Our method does not require any sampling and provably ensures that the DNN fulfills the affine constraint on a given input space's region at any point during training, and testing. We coin this method POLICE, standing for Provably Optimal LInear Constraint Enforcement.

Shapley values are ubiquitous in interpretable Machine Learning due to their strong theoretical background and efficient implementation in the SHAP library. Computing these values previously induced an exponential cost with respect to the number of input features of an opaque model. Now, with efficient implementations such as Interventional TreeSHAP, this exponential burden is alleviated assuming one is explaining ensembles of decision trees. Although Interventional TreeSHAP has risen in popularity, it still lacks a formal proof of how/why it works. We provide such proof with the aim of not only increasing the transparency of the algorithm but also to encourage further development of these ideas. Notably, our proof for Interventional TreeSHAP is easily adapted to Shapley-Taylor indices and one-hot-encoded features.

尽管自我监督学习（SSL）方法取得了经验成功，但尚不清楚其表示的哪些特征导致了高下游精度。在这项工作中，我们表征了SSL表示应该满足的属性。具体而言，我们证明了必要和充分的条件，因此，对于给出的数据增强的任何任务，在该表示形式上训练的所需探针（例如，线性或MLP）具有完美的准确性。这些要求导致一个统一的概念框架，用于改善现有的SSL方法并得出新方法。对于对比度学习，我们的框架规定了对以前的方法（例如使用不对称投影头）的简单但重大改进。对于非对比度学习，我们使用框架来得出一个简单新颖的目标。我们所得的SSL算法在标准基准测试上的表现优于基线，包括Imagenet线性探测的SHAV+多螺旋桨。