Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.). We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e.g. fruits, glasses, phones, etc.) that frequently change their positions due to human intervention. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability.

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

State space models (SSMs) have demonstrated state-of-the-art sequence modeling performance in some modalities, but underperform attention in language modeling. Moreover, despite scaling nearly linearly in sequence length instead of quadratically, SSMs are still slower than Transformers due to poor hardware utilization. In this paper, we make progress on understanding the expressivity gap between SSMs and attention in language modeling, and on reducing the hardware barrier between SSMs and attention. First, we use synthetic language modeling tasks to understand the gap between SSMs and attention. We find that existing SSMs struggle with two capabilities: recalling earlier tokens in the sequence and comparing tokens across the sequence. To understand the impact on language modeling, we propose a new SSM layer, H3, that is explicitly designed for these abilities. H3 matches attention on the synthetic languages and comes within 0.4 PPL of Transformers on OpenWebText. Furthermore, a hybrid 125M-parameter H3-attention model that retains two attention layers surprisingly outperforms Transformers on OpenWebText by 1.0 PPL. Next, to improve the efficiency of training SSMs on modern hardware, we propose FlashConv. FlashConv uses a fused block FFT algorithm to improve efficiency on sequences up to 8K, and introduces a novel state passing algorithm that exploits the recurrent properties of SSMs to scale to longer sequences. FlashConv yields 2$\times$ speedup on the long-range arena benchmark and allows hybrid language models to generate text 1.6$\times$ faster than Transformers. Using FlashConv, we scale hybrid H3-attention language models up to 1.3B parameters on the Pile and find promising initial results, achieving lower perplexity than Transformers and outperforming Transformers in zero- and few-shot learning on a majority of tasks in the SuperGLUE benchmark.

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. In each language, it contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location and Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language sentence. We also create manually annotated testsets for 8 languages containing approximately 1000 sentences per language. We demonstrate the utility of the obtained dataset on existing testsets and the Naamapadam-test data for 8 Indic languages. We also release IndicNER, a multilingual mBERT model fine-tuned on the Naamapadam training set. IndicNER achieves the best F1 on the Naamapadam-test set compared to an mBERT model fine-tuned on existing datasets. IndicNER achieves an F1 score of more than 80 for 7 out of 11 Indic languages. The dataset and models are available under open-source licenses at https://ai4bharat.iitm.ac.in/naamapadam.

与仅对面部进行建模的早期方法相比，最近的3D面部重建方法重建了整个头部。尽管这些方法准确地重建了面部特征，但它们并未明确调节头部的上部。由于头发的闭塞程度不同，提取有关头部这一部分的信息具有挑战性。我们提出了一种新颖的方法，可以通过去除遮挡头发并重建皮肤，从而揭示有关头部形状的信息来建模上头。我们介绍了三个目标：1）骰子一致性损失，该骰子一致性损失在源的整体形状和渲染图像之间强制相似，2）刻度一致性损失，以确保即使头部的上部不是头部，也可以准确地复制头部形状可见，3）使用移动平均损耗功能训练的71个地标探测器，以检测头部的其他地标。这些目标用于以无监督的方式训练编码器，以从野外输入图像中回归火焰参数。我们无监督的3MM模型可在流行的基准上实现最新的结果，可用于推断动画或阿凡达创建中直接使用的头部形状，面部特征和纹理。

这项调查表明，在算术电路复杂性，结构化矩阵和深度学习的交集中，一定是不完整的（偏见）概述的结果。最近，有一些研究活动在通过结构化的网络中代替神经网络中的非结构化重量矩阵（目的是减少相应的深度学习模型的大小）。这项工作的大部分都是实验性的，在这项调查中，我们将研究问题正式化，并展示了最新的工作如何结合算术电路复杂性，结构化矩阵和深度学习，从本质上回答了这个问题。这项调查针对的是复杂的理论家，他们可能喜欢阅读有关算术电路复杂性中开发的工具如何帮助设计（据我们所知）一个新的结构化矩阵家族，这反过来又非常适合深度学习。但是，我们希望主要对深度学习感兴趣的人们也会欣赏与复杂性理论的联系。

线性时间不变的状态空间模型（SSM）是工程和统计数据的经典模型，最近通过结构化状态空间序列模型（S4）证明，在机器学习中非常有前途。 S4的核心成分涉及将SSM状态矩阵初始化为称为HIPPO矩阵的特定矩阵，这对于S4处理长序列的能力在经验上很重要。但是，S4使用的特定矩阵实际上是在特定时间变化的动态系统中得出的，并且将此矩阵用作时间不变的SSM没有已知的数学解释。因此，S4模拟远程依赖性的理论机制实际上仍无法解释。我们得出了河马框架的更一般和直观的公式，该框架将S4作为对指数型的Legendre多项式的分解提供了简单的数学解释，解释了其捕获长依赖性的能力。我们的概括引入了理论上丰富的SSM类，还使我们能够为其他碱基（例如傅立叶基础）得出更直观的S4变体，并解释了训练S4的其他方面，例如如何初始化重要的时间表参数。这些见解将S4的性能提高到远程竞技场基准的86％，在最困难的Path-X任务中，S4的性能为96％。

在本文中，我们介绍了一项针对INLG 2022代挑战（Genchal）提交的系统，该系统涉及对合成的质量评估合成生成的代码混合的Hinglish文本的质量评估。我们实施了基于BISTM的神经网络模型，以预测合成Hinglish数据集的平均评分评分和分歧分数。在我们的模型中，我们将单词嵌入式用于英语和印地语数据，以及用于Hinglish Data的热门编码。我们在平均评分评分预测任务中达到了0.11的F1分数，平均平方误差为6.0。在分歧分数预测的任务中，我们的F1得分为0.18，平均误差为5.0。

变压器在长序列上是缓慢的，渴望记忆力，因为自我注意的时间和记忆复杂性在序列上是二次的。近似关注方法试图通过交易模型质量以降低计算复杂性来解决此问题，但通常无法实现墙壁锁定的加速。我们认为，缺失的原则是提出注意力算法，以考虑读取和在GPU记忆层次之间写入。我们提出了FlashAttention，这是一种IO意识的精确注意算法，该算法使用平铺来减少GPU高带宽内存（HBM）和GPU芯片SRAM之间的内存读数/写入/写入。我们分析了闪存的IO复杂性，表明它所需的HBM访问少于标准注意力，并且对于一系列SRAM尺寸而言是最佳的。我们还扩展了闪光词，以引起障碍物的注意，从而产生了比任何现有的近似关注方法更快的近似关注算法。闪存火车的变压器​​比现有基准快：与MLPERF 1.1训练速度记录相比，Bert-Large（第512秒）的端到端壁式锁定加速度为15％，GPT-2上的3 $ \ times $ speedup（seq） 。闪存表现和块状闪光词可在变压器中实现更长的上下文，从而产生更高质量的模型（GPT-2上的0.7更好的困惑和长期分类的6.4点升力）和全新的功能：第一个实现优于更好的Chance的变压器PATH-X挑战（Seq。Length16K，61.4％精度）和PATH-256（Seq。Length64K，63.1％精度）上的性能。

过度分辨的神经网络概括井，但训练昂贵。理想情况下，人们希望减少其计算成本，同时保留其概括的益处。稀疏的模型培训是实现这一目标的简单和有希望的方法，但随着现有方法与准确性损失，慢速训练运行时的困难或困难，仍然存在挑战，仍然存在困难的挑战。核心问题是，在离散的一组稀疏矩阵上搜索稀疏性掩模是困难和昂贵的。为了解决此问题，我们的主要见解是通过具有称为蝴蝶矩阵产品的固定结构的固定结构来优化优化稀疏矩阵的连续超集。随着蝴蝶矩阵不是硬件效率，我们提出了简单的蝴蝶（块和平坦）的变体来利用现代硬件。我们的方法（像素化蝴蝶）使用基于扁平块蝴蝶和低秩矩阵的简单固定稀疏模式，以缩小大多数网络层（例如，注意，MLP）。我们经验验证了像素化蝴蝶比蝴蝶快3倍，加快培训，以实现有利的准确性效率权衡。在ImageNet分类和Wikitext-103语言建模任务中，我们的稀疏模型训练比致密的MLP - 混频器，视觉变压器和GPT-2媒体更快地训练高达2.5倍，没有精确下降。