我们研究在线交互式强盗设置中的非模块化功能。我们是受到某些元素之间自然互补性的应用程序的动机：这仅使用只能代表元素之间竞争力的下函数来表达这一点。我们通过两种方式扩展了纯粹的下二次方法。首先，我们假设该物镜可以分解为单调下模量和超模块函数的总和，称为BP物镜。在这里，互补性自然是由超模型成分建模的。我们开发了UCB风格的算法，在每一轮比赛中，在采取行动以平衡对未知目标（探索）和选择似乎有希望的行动（剥削）的行动之间揭示的嘈杂收益。根据全知识的贪婪基线来定义遗憾和超模块化曲率，我们表明该算法最多可以在$ o（\ sqrt {t}）$ hore $ t $ t $ t $ the $ t $ t $ the $ t $ t $ the $ the。其次，对于那些不承认BP结构的功能，我们提供了类似的遗憾保证，从其表现比率角度来看。这适用于几乎但不完全是子模型的功能。我们在数值上研究了Movielens数据集上电影推荐的任务，并选择用于分类的培训子集。通过这些示例，我们证明了该算法的性能以及将这些问题视为单次生管的缺点。

本文探讨了一种机器学习方法，用于从单芯片MMWave雷达产生高分辨率点云。与激光雷达和基于视觉的系统不同，MMWave雷达可以在恶劣的环境中运行，并通过烟雾，雾气和灰尘等遮挡。不幸的是，与激光点云相比，当前的MMWAVE处理技术可提供差的空间分辨率。本文介绍了Radarhd，这是一种端到端的神经网络，该网络从低分辨率雷达输入中构造了激光雷达点云。由于存在镜面和虚假的反射，增强雷达图像是具有挑战性的。由于信号的类似SINC的扩展模式，雷达数据也不能很好地映射到传统的图像处理技术。我们通过在大量的RAW I/Q雷达数据上训练Radarhd与各种室内环境中的LiDar Point云配对来克服这些挑战。我们的实验表明，即使在训练期间未观察到的场景和存在浓烟的情况下，也能够产生丰富的点云。此外，Radarhd的点云足够高，足以与现有的LiDAR ODOMETIRE和映射工作流程配合使用。

随着数据集大小的不断增加，子集选择技术对于普遍的任务变得越来越重要。通常需要引导子集选择以实现某些探索，其中包括聚焦或针对某些数据点，同时避免他人。这些问题的示例包括：i）目标学习，目标是找到具有罕见类或稀有属性的子集，其中模型表现不佳，II）引导摘要，其中数据（例如，图像集合，文本，文档或视频） ）总结了以更快的人类消费与特定的额外用户意图更快。受此类应用程序的动机，我们呈现棱镜，丰富的参数化子模块信息措施。通过小说函数及其参数化，PRISM提供了各种建模能力，该模型能力使得在子集的所需质量之间具有权衡，例如具有一组数据点的分集或表示和相似性/相似性。我们展示了如何应用于上面提到的两个真实问题的棱镜，这需要引导子集选择。在这样做时，我们表明棱镜有趣地概括了一些过去的工作，在其中加强了其广泛的效用。通过对不同数据集的广泛实验，我们展示了棱镜的优越性，在目标学习和引导的图像收集概述中

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

Partial differential equations (PDEs) are widely used for description of physical and engineering phenomena. Some key parameters involved in PDEs, which represents certain physical properties with important scientific interpretations, are difficult or even impossible to be measured directly. Estimation of these parameters from noisy and sparse experimental data of related physical quantities is an important task. Many methods for PDE parameter inference involve a large number of evaluations of numerical solution of PDE through algorithms such as finite element method, which can be time-consuming especially for nonlinear PDEs. In this paper, we propose a novel method for estimating unknown parameters in PDEs, called PDE-Informed Gaussian Process Inference (PIGPI). Through modeling the PDE solution as a Gaussian process (GP), we derive the manifold constraints induced by the (linear) PDE structure such that under the constraints, the GP satisfies the PDE. For nonlinear PDEs, we propose an augmentation method that transfers the nonlinear PDE into an equivalent PDE system linear in all derivatives that our PIGPI can handle. PIGPI can be applied to multi-dimensional PDE systems and PDE systems with unobserved components. The method completely bypasses the numerical solver for PDE, thus achieving drastic savings in computation time, especially for nonlinear PDEs. Moreover, the PIGPI method can give the uncertainty quantification for both the unknown parameters and the PDE solution. The proposed method is demonstrated by several application examples from different areas.

Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a \emph{generative model}. We propose the AE-LSVI algorithm for best-policy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy \emph{uniformly} over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required.

Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem. However, linear programming algorithms and approximate OT solvers via Sinkhorn for computing the permutation matrix come with a significant computational burden since they scale cubically and quadratically, respectively, in the input size. This makes it slow and infeasible to compute OT distances exactly for a larger input size, resulting in a poor approximation quality of the permutation matrix and subsequently a less robust learned transfer function or mapper. This paper proposes an unsupervised projection-based CLWE model called quantized Wasserstein Procrustes (qWP). qWP relies on a quantization step of both the source and target monolingual embedding space to estimate the permutation matrix given a cheap sampling procedure. This approach substantially improves the approximation quality of empirical OT solvers given fixed computational cost. We demonstrate that qWP achieves state-of-the-art results on the Bilingual lexicon Induction (BLI) task.

We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths. Better understanding of the engineering tradeoffs for inference for large Transformer-based models is important as use cases of these models are growing rapidly throughout application areas. We develop a simple analytical model for inference efficiency to select the best multi-dimensional partitioning techniques optimized for TPU v4 slices based on the application requirements. We combine these with a suite of low-level optimizations to achieve a new Pareto frontier on the latency and model FLOPS utilization (MFU) tradeoffs on 500B+ parameter models that outperforms the FasterTransformer suite of benchmarks. We further show that with appropriate partitioning, the lower memory requirements of multiquery attention (i.e. multiple query heads share single key/value head) enables scaling up to 32x larger context lengths. Finally, we achieve a low-batch-size latency of 29ms per token during generation (using int8 weight quantization) and a 76% MFU during large-batch-size processing of input tokens, while supporting a long 2048-token context length on the PaLM 540B parameter model.

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.