我们呈现Turbo-SIM，是可以用作生成模型的信息理论原理的广义自动统计学框架。通过最大化输入和编码器和解码器的输出之间的相互信息，我们能够重新发现通常在对手自身额外的损失术语和生成的对抗网络中发现的损失术语，以及各种更复杂的相关模型。我们的广义框架使这些模型在数学上解释，通过分别设置每个损失项的重量来允许新的新功能。该框架还与编码器的内在架构和解码器无关，因此为整个网络的构建块留下了广泛的选择。我们将Turbo-SIM应用于碰撞机物理生成问题：在实验中检测到检测后，在碰撞之后，在碰撞之后的理论空间，在观察空间之后，从理论空间转换几个粒子的性质。

条件生成是生成问题的子类，其中生成的输出由属性信息调节。在本文中，我们提出了一种随机对比条件生成的对抗网络（InfoSCC-GaN），具有易诺的潜在空间。 InfoSCC-GaN架构基于内置于Infonce Paradigm的无监督对比编码器，属性分类器和Eigengan生成器。我们提出了一种新颖的训练方法，基于每次$ N $第-th迭代的外部或内部属性使用外部或内部属性，使用预先培训的对比编码器和预先训练的分类器。基于输入数据和潜在空间表示之间的相互信息最大化以及潜在空间和生成的数据来导出所提出的INFOSCC-GAN。因此，我们展示了训练目标函数与上述信息理论制剂之间的联系。实验结果表明，InfoSCC-GaN在AFHQ和Celeba数据集上的图像生成中优于“vanilla”Eigengan。此外，我们通过进行消融研究调查鉴别员架构和损失功能的影响。最后，我们证明，由于eigengan发电机，所提出的框架与Vanilla确定性GAN相比，与现有框架相比，与Vanilla确定性GAN相比，与Vanilla确定性GAN相反。代码，实验结果和演示可在HTTPS://github.com/vkinakh/infoscc-在线提供。

不连续分布的生成是大多数已知框架的困难任务，例如生成的自动化器和生成的对抗网络。生成的非可逆模型无法准确地生成此类分布，需要长期训练，并且经常受模式崩溃。变形AutoEncoders（VAES），基于保持潜在空间的想法是为了简单的采样，允许准确的重建，同时在生成任务中遇到重大限制。在这项工作中，我们使用预先训练的对比编码器来获得聚类潜空间来保持潜在的空间。然后，对于每个群集表示单向子多种子区，我们训练专用的低复杂性网络以从高斯分布生成该子多种。所提出的框架基于输入数据和潜在空间表示之间的相互信息最大化的信息定理制定。我们派生了成本函数与信息理论制定之间的联系。我们将我们的方法应用于合成2D分布，以展示使用连续随机网络的重建和产生不连续分布的方法。

In this paper, we present a framework for learning quadruped navigation by integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework. Through both exteroceptive and proprioceptive sensing, the agent learns to modulate the intrinsic oscillator setpoints (amplitude and frequency) and coordinate rhythmic behavior among different oscillators to track velocity commands while avoiding collisions with the environment. We compare different neural network architectures (i.e. memory-free and memory-enabled) which learn implicit interoscillator couplings, as well as varying the strength of the explicit coupling weights in the oscillator dynamics equations. We train our policies in simulation and perform a sim-to-real transfer to the Unitree Go1 quadruped, where we observe robust navigation in a variety of scenarios. Our results show that both memory-enabled policy representations and explicit interoscillator couplings are beneficial for a successful sim-to-real transfer for navigation tasks. Video results can be found at https://youtu.be/O_LX1oLZOe0.

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance.

Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of agents' actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, discontinuous bandit Q-learning, in view of a potential photonic implementation. Here, state-action pairs in the environment are regarded as slot machines in the context of the bandit problem and an updated amount of Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents. We demonstrate that multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.

Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change.

We discuss pattern languages for closed pattern mining and learning of interval data and distributional data. We first introduce pattern languages relying on pairs of intersection-based constraints or pairs of inclusion based constraints, or both, applied to intervals. We discuss the encoding of such interval patterns as itemsets thus allowing to use closed itemsets mining and formal concept analysis programs. We experiment these languages on clustering and supervised learning tasks. Then we show how to extend the approach to address distributional data.

The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that transformers are capable of high accuracy in varied agreement tasks, but the mechanisms by which the models accomplish this behavior are still not well understood. To better understand transformers' internal working, this work contrasts how they handle two superficially similar but theoretically distinct agreement phenomena: subject-verb and object-past participle agreement in French. Using probing and counterfactual analysis methods, our experiments show that i) the agreement task suffers from several confounders which partially question the conclusions drawn so far and ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.

Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are typically trained end-to-end on protein complex structures extracted from the Protein Data Bank. These training datasets tend to be large and difficult to use for prototyping and, unlike image or natural language datasets, they are not easily interpretable by non-experts. We present Dock2D-IP and Dock2D-IF, two "toy" datasets that can be used to select algorithms predicting protein-protein interactions$\unicode{x2014}$or any other type of molecular interactions. Using two-dimensional shapes as input, each example from Dock2D-IP ("interaction pose") describes the interaction pose of two shapes known to interact and each example from Dock2D-IF ("interaction fact") describes whether two shapes form a stable complex or not. We propose a number of baseline solutions to the problem and show that the same underlying energy function can be learned either by solving the interaction pose task (formulated as an energy-minimization "docking" problem) or the fact-of-interaction task (formulated as a binding free energy estimation problem).