The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept of end-to-end learning of optical flow and make it work really well. The large improvements in quality and speed are caused by three major contributions: first, we focus on the training data and show that the schedule of presenting data during training is very important. Second, we develop a stacked architecture that includes warping of the second image with intermediate optical flow. Third, we elaborate on small displacements by introducing a subnetwork specializing on small motions. FlowNet 2.0 is only marginally slower than the original FlowNet but decreases the estimation error by more than 50%. It performs on par with state-of-the-art methods, while running at interactive frame rates. Moreover, we present faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet.

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT benefits from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we propose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. Finally, TaMOs achieves highly competitive results on single-object GOT datasets, setting a new state-of-the-art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.

A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.

Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitatively distinct neural network models often predict similar representational geometries of typical stimulus sets. We propose a Bayesian experimental design approach to synthesizing stimulus sets for adjudicating among representational models efficiently. We apply our method to discriminate among candidate neural network models of behavioral face dissimilarity judgments. Our results indicate that a neural network trained to invert a 3D-face-model graphics renderer is more human-aligned than the same architecture trained on identification, classification, or autoencoding. Our proposed stimulus synthesis objective is generally applicable to designing experiments to be analyzed by representational similarity analysis for model comparison.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

我们提供了奖励黑客的第一个正式定义，即优化不完美的代理奖励功能的现象，$ \ Mathcal {\ tilde {r}} $，根据真实的奖励功能，$ \ MATHCAL {R} $导致性能差。 。我们说，如果增加预期的代理回报率永远无法减少预期的真实回报，则代理是不可接受的。直觉上，可以通过从奖励功能（使其“较窄”）中留出一些术语或忽略大致等效的结果之间的细粒度区分来创建一个不可接受的代理，但是我们表明情况通常不是这样。一个关键的见解是，奖励的线性性（在州行动访问计数中）使得无法实现的状况非常强烈。特别是，对于所有随机策略的集合，只有在其中一个是恒定的，只有两个奖励函数才能是不可接受的。因此，我们将注意力转移到确定性的政策和有限的随机政策集中，在这些策略中，始终存在非平凡的不可动摇的对，并为简化的存在建立必要和充分的条件，这是一个重要的不被限制的特殊情况。我们的结果揭示了使用奖励函数指定狭窄任务和对齐人类价值的AI系统之间的紧张关系。

我们提出了Zeroeggs，这是一个神经网络框架，用于语音驱动的手势生成，以零拍出样式控制。这意味着即使在训练过程中看不见的运动样式，也只能通过一个简短的运动剪辑来控制样式。我们的模型使用一个变性框架来学习样式嵌入，从而可以通过潜在的空间操纵或样式嵌入方式的混合和缩放来修改样式。我们框架的概率性质进一步使给定输入相同的各种输出的产生，以解决手势运动的随机性质。在一系列实验中，我们首先证明了模型对新的扬声器和样式的灵活性和概括性。然后，在一项用户研究中，我们表明我们的模型在运动，语音适当性和风格刻画方面的自然性，适当性和刻画的表现优于先前的最先进技术。最后，我们释放了包括手指在内的全身手势运动的高质量数据集，语音跨越了19种不同的样式。

当今，机器人技术的新型机器人运动学和基于学习的应用程序的开发几乎完全在模拟中进行，然后才在现实世界中实施。特别是，与传统的操纵器相比，模块化可重构机器人（MRR）是工业机器人技术的令人兴奋的创新，有望更大的灵活性，提高可维护性和成本效益。但是，几十年来，没有像为机器人操纵器对模块进行模拟和模型组件的工具或标准化方法。我们介绍了工业模块化机器人技术的工具箱（Timor），这是一种python工具箱，可弥合此间隙并将模块化机器人技术集成在现有的仿真和优化管道中。我们的开源库配备了各种示例和教程，并且可以轻松地与现有的仿真工具集成在一起 - 尤其是通过提供任意模块化机器人组件的URDF导出，从而使快速模型生成。