Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), owing to their excellent understanding and generation abilities. Remarkably, what further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. While many downstream applications provide the model with an informational context to aid its performance on the underlying task, how the model's world knowledge interacts with the factual information presented in the context remains under explored. As a desirable behavior, an LLM should give precedence to the context whenever it contains task-relevant information that conflicts with the model's memorized knowledge. This enables model predictions to be grounded in the context, which can then be used to update or correct specific model predictions without frequent retraining. By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge. In this paper, we undertake a first joint study of the aforementioned two properties, namely controllability and robustness, in the context of LLMs. We demonstrate that state-of-the-art T5 and PaLM (both pretrained and finetuned) could exhibit poor controllability and robustness, which do not scale with increasing model size. As a solution, we propose a novel method - Knowledge Aware FineTuning (KAFT) - to strengthen both controllability and robustness by incorporating counterfactual and irrelevant contexts to standard supervised datasets. Our comprehensive evaluation showcases the utility of KAFT across model architectures and sizes.

任务计划可能需要定义有关机器人需要采取行动的世界的无数领域知识。为了改善这项工作，可以使用大型语言模型（LLM）在任务计划期间为潜在的下一个操作评分，甚至直接生成动作序列，鉴于没有其他域信息的自然语言指令。但是，这样的方法要么需要列举所有可能的下一步评分，要么生成可能包含在当前机器人中给定机器人上不可能操作的自由形式文本。我们提出了一个程序化的LLM提示结构，该结构能够跨越位置环境，机器人功能和任务的计划生成功能。我们的关键见解是提示LLM具有环境中可用操作和对象的类似程序的规格，以及可以执行的示例程序。我们通过消融实验提出了有关迅速结构和生成约束的具体建议，证明了虚拟屋家庭任务中最先进的成功率，并将我们的方法部署在桌面任务的物理机器人组上。网站progprompt.github.io

大型预估计模型（例如GPT-3）取得了显着的性能，在训练过程中暴露于大量数据上。类似地，将如此大型模型提炼成紧凑的模型以进行有效的部署，也需要大量（标记或未标记的）培训数据。在本文中，我们提出了培训高质量紧凑型模型的教师指导培训（TGT）框架，该模型利用了预验证的生成模型获得的知识，同时避免了大量数据的需求。 TGT利用了教师获得基础数据域的良好表示的事实，该事实通常对应于比输入空间要低得多的尺寸歧管。此外，我们可以使用老师通过采样或基于梯度的方法来更有效地探索输入空间。因此，使TGT对于有限的数据或长尾设置特别有吸引力。我们正式在我们的概括范围内正式捕获了所提出的数据域探索的好处。我们发现TGT可以提高几个图像分类基准以及一系列文本分类和检索任务的准确性。

无监督的域适应（UDA）旨在将标记的源分布与未标记的目标分布对齐，以获取域不变预测模型。然而，众所周知的UDA方法的应用在半监督域适应（SSDA）方案中不完全概括，其中来自目标域的少数标记的样本可用。在本文中，我们提出了一种用于半监督域适应（CLDA）的简单对比学习框架，该框架试图在SSDA中弥合标记和未标记的目标分布与源极和未标记的目标分布之间的域间差距之间的域间隙。我们建议采用类明智的对比学学习来降低原始（输入图像）和强大增强的未标记目标图像之间的域间间隙和实例级对比度对准，以最小化域内差异。我们已经凭经验表明，这两个模块相互补充，以实现卓越的性能。在三个众所周知的域适应基准数据集中的实验即Domainnet，Office-Home和Office31展示了我们方法的有效性。 CLDA在所有上述数据集上实现最先进的结果。

Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples. This poses a challenge for generalisation on such labels, and also makes naïve learning biased towards dominant labels. In this paper, we present two simple modifications of standard softmax cross-entropy training to cope with these challenges. Our techniques revisit the classic idea of logit adjustment based on the label frequencies, either applied post-hoc to a trained model, or enforced in the loss during training. Such adjustment encourages a large relative margin between logits of rare versus dominant labels. These techniques unify and generalise several recent proposals in the literature, while possessing firmer statistical grounding and empirical performance. A reference implementation of our methods is available at: https://github.com/google-research/google-research/tree/master/logit_adjustment.Recently, long-tail learning has received renewed interest in the context of neural networks. Two active strands of work involve post-hoc normalisation of the classification weights [

Pennylane是用于量子计算机可区分编程的Python 3软件框架。该库为近期量子计算设备提供了统一的体系结构，支持量子和连续变化的范例。 Pennylane的核心特征是能够以与经典技术（例如反向传播）兼容的方式来计算变异量子电路的梯度。因此，Pennylane扩展了在优化和机器学习中常见的自动分化算法，以包括量子和混合计算。插件系统使该框架与任何基于门的量子模拟器或硬件兼容。我们为硬件提供商提供插件，包括Xanadu Cloud，Amazon Braket和IBM Quantum，允许Pennylane优化在公开访问的量子设备上运行。在古典方面，Pennylane与加速的机器学习库（例如Tensorflow，Pytorch，Jax和Autograd）接口。 Pennylane可用于优化变分的量子本素体，量子近似优化，量子机学习模型和许多其他应用。

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.

We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.