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.

多视图检测（MVD）对于拥挤环境中的遮挡推理非常有效。虽然最近使用深度学习的作品在该领域取得了重大进展，但它们已经忽略了泛化方面，这使得它们\ emph {现实世界部署不切实际。我们工作的关键新颖性是\ emph {形式化}三种临界形式的普遍化和\ emph {建议实验来评估它们}：泛化与i）不同数量的相机，ii）变化的相机位置，最后，iii）到新场景。我们发现现有的最先进的模型通过对单个场景和相机配置过度提供了较差的概括。为了解决问题：（a）我们提出了一种新颖的通用MVD（GMVD）数据集，同时使用变化的日间，相机配置，不同数量的相机以及（B）来吸收多样化的场景，以及（B）我们讨论了对MVD带来概括的属性并提出一个鞍座模型融合它们。我们在WildTrack，MultiviewX和GMVD数据集上执行一套全面的实验，以激励评估MVD方法的概括能力，并证明所提出的方法的功效。可以在\ url {https：github.com/jeetv/gmvd}中找到代码和建议的数据集

全息减少的表示（HRR）是通过将每个向量与抽象概念相关联，并提供数学操作以操纵向量的方法来执行符号AI的方法，以便操纵向量，就像它们是经典的符号对象一样。这种方法在较旧的象征性AI工作和认知科学之外已经很少使用。我们的目标是重新审视这种方法，以了解它是否可行，以使混合神经象征性的方法能够学习作为深度学习架构的可差分量。由于数值不稳定性，HRRS今天在可分辨率的解决方案中无效，我们通过引入迫使向量存在于空间良好的点中的投影步骤来解决问题。这样做，我们将HRRS的概念检索效果提高超过100美元。使用多标签分类，我们演示了如何利用符号HRR属性来开发能够有效学习的输出层和损耗功能，并允许我们调查HRR神经象征性学习方法的一些优缺点。我们的代码可以在https://github.com/neuromorphiccomputationResearchProgram/learning-with-hotographicuredued-representations

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that - across 6 architectures trained with 4 algorithms on massive datasets - they exhibit little compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark CREPE which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of three test datasets. The three test sets are designed to test models trained on three of the popular training datasets: CC-12M, YFCC-15M, and LAION-400M. They contain 385K, 385K, and 373K image-text pairs and 237K, 210K, and 178K hard negative captions. To test productivity, CREPE contains 17K image-text pairs with nine different complexities plus 246K hard negative captions with atomic, swapping, and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to 8%. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size.

This paper proposes embedded Gaussian Process Barrier States (GP-BaS), a methodology to safely control unmodeled dynamics of nonlinear system using Bayesian learning. Gaussian Processes (GPs) are used to model the dynamics of the safety-critical system, which is subsequently used in the GP-BaS model. We derive the barrier state dynamics utilizing the GP posterior, which is used to construct a safety embedded Gaussian process dynamical model (GPDM). We show that the safety-critical system can be controlled to remain inside the safe region as long as we can design a controller that renders the BaS-GPDM's trajectories bounded (or asymptotically stable). The proposed approach overcomes various limitations in early attempts at combining GPs with barrier functions due to the abstention of restrictive assumptions such as linearity of the system with respect to control, relative degree of the constraints and number or nature of constraints. This work is implemented on various examples for trajectory optimization and control including optimal stabilization of unstable linear system and safe trajectory optimization of a Dubins vehicle navigating through an obstacle course and on a quadrotor in an obstacle avoidance task using GP differentiable dynamic programming (GP-DDP). The proposed framework is capable of maintaining safe optimization and control of unmodeled dynamics and is purely data driven.

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.

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.

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4\%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.

我们在室外环境中自动驾驶的背景下研究了视觉和语言导航（VLN）问题。我们通过明确接地与Textual命令相对应的可通道区域来解决问题。在每个时间戳，该模型预测与中间或最终可通道区域相对应的分割掩码。我们的工作与VLN中的现有工作形成鲜明对比，VLN的现有工作将该任务置于节点选择问题，并且给定与环境相对应的离散连接图。我们不假定这种离散的地图的可用性。我们的工作朝着动作领域的连续性发展，通过视觉反馈提供了解释性，并允许在需要更精细的操作的命令上进行VLN，例如“两辆汽车之间的停车”。此外，我们提出了一种新型的元数据carla-nav，以允许有效的训练和验证。该数据集包括预录制的培训序列以及用于验证和测试的实时环境。我们提供广泛的定性和定量经验结果，以验证所提出的方法的功效。

培训和评估机器学习模型的迭代是提高其性能的重要过程。但是，尽管可教学的接口使盲人用户能够在其独特的环境中拍摄的照片训练和测试对象识别器，但训练迭代和评估步骤的可访问性很少受到关注。迭代假设训练照片的目视检查，对于盲人用户来说是无法访问的。我们通过MyCam探索了这一挑战，Mycam是一个移动应用程序，该应用程序合并了自动估计的描述符，以在用户培训集中对照片进行非视觉访问。我们探索盲人参与者（n = 12）如何通过他们的家中的评估研究与mycam和描述符相互作用。我们证明，实时照片级描述符使盲人用户能够用裁剪的对象减少照片，并且参与者可以通过迭代并访问其训练集的质量来增加更多的变化。此外，参与者发现该应用程序易于使用，表明他们可以有效地训练它，并且描述符很有用。但是，主观反应并未反映在其模型的性能中，部分原因是训练和混乱背景的变化很小。