我们提出了Rapid-Learn：学习再次恢复和计划，即一种混合计划和学习方法，以解决适应代理环境中突然和意外变化（即新颖性）的问题。 Rapid-Learn旨在实时制定和求解任务的Markov决策过程（MDPS），并能够利用域知识来学习由环境变化引起的任何新动态。它能够利用域知识来学习行动执行者，这可以进一步用于解决执行智能，从而成功执行了计划。这种新颖信息反映在其更新的域模型中。我们通过在受到Minecraft启发的环境环境中引入各种新颖性来证明其功效，并将我们的算法与文献中的转移学习基线进行比较。我们的方法是（1）即使在存在多个新颖性的情况下，（2）比转移学习RL基准的样本有效，以及（3）与不完整的模型信息相比，与纯净的符号计划方法相反。

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

Radiance Fields (RF) are popular to represent casually-captured scenes for new view generation and have been used for applications beyond it. Understanding and manipulating scenes represented as RFs have to naturally follow to facilitate mixed reality on personal spaces. Semantic segmentation of objects in the 3D scene is an important step for that. Prior segmentation efforts using feature distillation show promise but don't scale to complex objects with diverse appearance. We present a framework to interactively segment objects with fine structure. Nearest neighbor feature matching identifies high-confidence regions of the objects using distilled features. Bilateral filtering in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., moving closer to rich scene manipulation and understanding. Project Page: https://rahul-goel.github.io/isrf/

Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition about these perturbations and the formal similarity specifications capturing them. While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e.g., in cases of asymmetric counterfactuals). This work proposes novel methods for bridging this gap by discovering expressive and intuitive individual fairness specifications. We show how to leverage unsupervised style transfer and GPT-3's zero-shot capabilities to automatically generate expressive candidate pairs of semantically similar sentences that differ along sensitive attributes. We then validate the generated pairs via an extensive crowdsourcing study, which confirms that a lot of these pairs align with human intuition about fairness in the context of toxicity classification. Finally, we show how limited amounts of human feedback can be leveraged to learn a similarity specification that can be used to train downstream fairness-aware models.

Stylized view generation of scenes captured casually using a camera has received much attention recently. The geometry and appearance of the scene are typically captured as neural point sets or neural radiance fields in the previous work. An image stylization method is used to stylize the captured appearance by training its network jointly or iteratively with the structure capture network. The state-of-the-art SNeRF method trains the NeRF and stylization network in an alternating manner. These methods have high training time and require joint optimization. In this work, we present StyleTRF, a compact, quick-to-optimize strategy for stylized view generation using TensoRF. The appearance part is fine-tuned using sparse stylized priors of a few views rendered using the TensoRF representation for a few iterations. Our method thus effectively decouples style-adaption from view capture and is much faster than the previous methods. We show state-of-the-art results on several scenes used for this purpose.

Indian e-commerce industry has evolved over the last decade and is expected to grow over the next few years. The focus has now shifted to turnaround time (TAT) due to the emergence of many third-party logistics providers and higher customer expectations. The key consideration for delivery providers is to balance their overall operating costs while meeting the promised TAT to their customers. E-commerce delivery partners operate through a network of facilities whose strategic locations help to run the operations efficiently. In this work, we identify the locations of hubs throughout the country and their corresponding mapping with the distribution centers. The objective is to minimize the total network costs with TAT adherence. We use Genetic Algorithm and leverage business constraints to reduce the solution search space and hence the solution time. The results indicate an improvement of 9.73% in TAT compliance compared with the current scenario.

Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories that increase the likelihood for an uncrewed aerial vehicle (UAV) to discover missing targets. Our approach efficiently (1) explores the environment to discover new targets, (2) exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification, and (3) generates dynamically feasible trajectories for an agile UAV by employing a motion primitive library. Extensive simulations on randomly generated environments show that our approach is more efficient in discovering and classifying targets than several other baselines. A unique characteristic of our approach, in contrast to heuristic informative path planning approaches, is that it is robust to varying amounts of deviations of the prior belief from the true target distribution, thereby alleviating the challenge of designing heuristics specific to the application conditions.

Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated that semantic parsing is a difficult multilingual transfer task with low transfer efficiency compared to other tasks. In global markets such as India and Latin America, this is a critical issue as switching between languages is prevalent for bilingual users. In this work we dramatically improve the zero-shot performance of a multilingual and codeswitched semantic parsing system using two stages of multilingual alignment. First, we show that constrastive alignment pretraining improves both English performance and transfer efficiency. We then introduce a constrained optimization approach for hyperparameter-free adversarial alignment during finetuning. Our Doubly Aligned Multilingual Parser (DAMP) improves mBERT transfer performance by 3x, 6x, and 81x on the Spanglish, Hinglish and Multilingual Task Oriented Parsing benchmarks respectively and outperforms XLM-R and mT5-Large using 3.2x fewer parameters.

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models

Automation in farming processes is a growing field of research in both academia and industries. A considerable amount of work has been put into this field to develop systems robust enough for farming. Terrace farming, in particular, provides a varying set of challenges, including robust stair climbing methods and stable navigation in unstructured terrains. We propose the design of a novel autonomous terrace farming robot, Aarohi, that can effectively climb steep terraces of considerable heights and execute several farming operations. The design optimisation strategy for the overall mechanical structure is elucidated. Further, the embedded and software architecture along with fail-safe strategies are presented for a working prototype. Algorithms for autonomous traversal over the terrace steps using the scissor lift mechanism and performing various farming operations have also been discussed. The adaptability of the design to specific operational requirements and modular farm tools allow Aarohi to be customised for a wide variety of use cases.