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AIOpsLab: Building AI agents for autonomous clouds
AIOpsLab is an open-source framework designed to evaluate and improve AI agents for cloud operations, offering standardized, scalable benchmarks for real-world testing, enhancing cloud system reliability.
The post AIOpsLab: Building AI agents for autonomous clouds appeared first on Microsoft Research.
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Ecologists find computer vision models’ blind spots in retrieving wildlife images
Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.
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Startup’s autonomous drones precisely track warehouse inventories
Corvus Robotics, founded by Mohammed Kabir ’21, is using drones that can navigate in GPS-denied environments to expedite inventory management.
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Transformer models are gauge invariant: A mathematical connection between AI and particle physics
arXiv:2412.14543v1 Announce Type: new
Abstract: In particle physics, the fundamental forces are subject to symmetries called gauge invariance. It is a redundancy in the mathematical description of any physical system. In this article I will demonstrate that the transformer architecture exhibits the same properties, and show that the default representation of transformers has partially, but not fully removed the gauge invariance.
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Trainable Adaptive Activation Function Structure (TAAFS) Enhances Neural Network Force Field Performance with Only Dozens of Additional Parameters
arXiv:2412.14655v1 Announce Type: new
Abstract: At the heart of neural network force fields (NNFFs) is the architecture of neural networks, where the capacity to model complex interactions is typically enhanced through widening or deepening multilayer perceptrons (MLPs) or by increasing layers of graph neural networks (GNNs). These enhancements, while improving the model's performance, often come at the cost of a substantial increase in the number of parameters. By applying the Trainable Adaptive Activation Function Structure (TAAFS), we introduce a method that selects distinct mathematical formulations for non-linear activations, thereby increasing the precision of NNFFs with an insignificant addition to the parameter count. In this study, we integrate TAAFS into a variety of neural network models, resulting in observed accuracy improvements, and further validate these enhancements through molecular dynamics (MD) simulations using DeepMD.
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Boosting GNN Performance via Training Sample Selection Based on Adversarial Robustness Evaluation
arXiv:2412.14738v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have established themselves as one of the most powerful neural network architectures, excelling in leveraging graph topology and node features for various tasks. However, GNNs are inherently vulnerable to noise in their inputs. Such noise can significantly degrade their performance. To address this challenge, we propose a novel approach that employs adversarial robustness evaluation techniques to identify nodes in the graph that are most susceptible to noise. By selecting and constructing a training set composed of these particularly noise-prone nodes, we then use them to train a Graph Convolutional Network (GCN). Our experimental results demonstrate that this strategy leads to substantial improvements in the GCN's performance.
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Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers
arXiv:2412.15077v1 Announce Type: new
Abstract: Today, deep neural networks are widely used since they can handle a variety of complex tasks. Their generality makes them very powerful tools in modern technology. However, deep neural networks are often overparameterized. The usage of these large models consumes a lot of computation resources. In this paper, we introduce a method called \textbf{T}ill the \textbf{L}ayers \textbf{C}ollapse (TLC), which compresses deep neural networks through the lenses of batch normalization layers. By reducing the depth of these networks, our method decreases deep neural networks' computational requirements and overall latency. We validate our method on popular models such as Swin-T, MobileNet-V2, and RoBERTa, across both image classification and natural language processing (NLP) tasks.
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Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate
arXiv:2412.14367v1 Announce Type: cross
Abstract: Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradients to train a neural network to act as a velocity controller for a quadcopter. The quadcopter's objective is to quickly fly through a gate while avoiding crashing into the gate. We transfer our trained policy to the real world by deploying it on a quadcopter in a laboratory environment. Finally, we demonstrate that the trained policy is able to navigate the drone to the gate in the real world.
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Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
arXiv:2412.14545v1 Announce Type: cross
Abstract: This study introduces a federated learning-based approach to predict HER2 status from hematoxylin and eosin (HE)-stained whole slide images (WSIs), reducing costs and speeding up treatment decisions. To address label imbalance and feature representation challenges in multisite datasets, a point transformer is proposed, incorporating dynamic label distribution, an auxiliary classifier, and farthest cosine sampling. Extensive experiments demonstrate state-of-the-art performance across four sites (2687 WSIs) and strong generalization to two unseen sites (229 WSIs).
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Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions
arXiv:2412.14750v1 Announce Type: cross
Abstract: Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and explore the impacts on $\Lambda$CDM cosmological parameters. Significant reductions in both Hubble ($H_0$) and clustering ($S_8$) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches.
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A Full Transformer-based Framework for Automatic Pain Estimation using Videos
arXiv:2412.15095v1 Announce Type: cross
Abstract: The automatic estimation of pain is essential in designing an optimal pain management system offering reliable assessment and reducing the suffering of patients. In this study, we present a novel full transformer-based framework consisting of a Transformer in Transformer (TNT) model and a Transformer leveraging cross-attention and self-attention blocks. Elaborating on videos from the BioVid database, we demonstrate state-of-the-art performances, showing the efficacy, efficiency, and generalization capability across all the primary pain estimation tasks.
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InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
arXiv:2311.16208v2 Announce Type: replace-cross
Abstract: The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data. Our novel contribution, InstructMol, a multi-modal LLM, effectively aligns molecular structures with natural language via an instruction-tuning approach, utilizing a two-stage training strategy that adeptly combines limited domain-specific data with molecular and textual information. InstructMol showcases substantial performance improvements in drug discovery-related molecular tasks, surpassing leading LLMs and significantly reducing the gap with specialized models, thereby establishing a robust foundation for a versatile and dependable drug discovery assistant.
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URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base
arXiv:2409.18472v2 Announce Type: replace-cross
Abstract: URIEL is a knowledge base offering geographical, phylogenetic, and typological vector representations for 7970 languages. It includes distance measures between these vectors for 4005 languages, which are accessible via the lang2vec tool. Despite being frequently cited, URIEL is limited in terms of linguistic inclusion and overall usability. To tackle these challenges, we introduce URIEL+, an enhanced version of URIEL and lang2vec that addresses these limitations. In addition to expanding typological feature coverage for 2898 languages, URIEL+ improves the user experience with robust, customizable distance calculations to better suit the needs of users. These upgrades also offer competitive performance on downstream tasks and provide distances that better align with linguistic distance studies.
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Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
arXiv:2412.14318v1 Announce Type: cross
Abstract: Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state is high dimensional, ensemble Kalman filters are often the method of choice. This paper establishes long-time accuracy of ensemble Kalman filters. We introduce conditions on the dynamics and the observations under which the estimation error remains small in the long-time horizon. Our theory covers a wide class of partially-observed chaotic dynamical systems, which includes the Navier-Stokes equations and Lorenz models. In addition, we prove long-time accuracy of ensemble Kalman filters with surrogate dynamics, thus validating the use of machine-learned forecast models in ensemble data assimilation.
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