Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

More complex data consisting of measurements across multiple modalities can be organized as higher dimensional data arrays, or higher order tensors.

Time series classification using deep neural networks, such as convolutional neural networks (CNN), operate on the spectral decomposition of the time series computed using a preprocessing step.

Monitoring feeding behaviour is a relevant task for efficient herd management and the effective use of available resources in grazing cattle. The ability to automatically recognise animals' feeding activities through the identification of specific jaw movements allows for the improvement of diet formulation, as well as early detection of metabolic problems and symptoms of animal discomfort, among other benefits. The use of sensors to obtain signals for such monitoring has become popular in the last two decades. The most frequently employed sensors include accelerometers, microphones, and cameras, each with its own set of advantages and drawbacks. An unexplored aspect is the simultaneous use of multiple sensors with the aim of combining signals in order to enhance the precision of the estimations. In this direction, this work introduces a deep neural network based on the fusion of acoustic and inertial signals, composed of convolutional, recurrent, and dense layers. The main advantage of this model is the combination of signals through the automatic extraction of features independently from each of them. The model has emerged from an exploration and comparison of different neural network architectures proposed in this work, which carry out information fusion at different levels. Feature-level fusion has outperformed data and decision-level fusion by at least a 0.14 based on the F1-score metric. Moreover, a comparison with state-of-the-art machine learning methods is presented, including traditional and deep learning approaches. The proposed model yielded an F1-score value of 0.802, representing a 14% increase compared to previous methods. Finally, results from an ablation study and post-training quantization evaluation are also reported.

Agent-based modeling is indispensable for studying complex systems across many domains. However, existing simulation platforms exhibit two major issues: performance and modularity. Low performance prevents simulations with a large number of agents, increases development time, limits parameter exploration, and raises computing costs. Inflexible software designs motivate modelers to create their own tools, diverting valuable resources. This dissertation introduces a novel simulation platform called BioDynaMo and its significant improvement, TeraAgent, to alleviate these challenges via three major works. First, we lay the platform's foundation by defining abstractions, establishing software infrastructure, and implementing a multitude of features for agent-based modeling. We demonstrate BioDynaMo's modularity through use cases in neuroscience, epidemiology, and oncology. We validate these models and show the simplicity of adding new functionality with few lines of code. Second, we perform a rigorous performance analysis and identify challenges for shared-memory parallelism. Provided solutions include an optimized grid for neighbor searching, mechanisms to reduce the memory access latency, and exploiting domain knowledge to omit unnecessary work. These improvements yield up to three orders of magnitude speedups, enabling simulations of 1.7 billion agents on a single server. Third, we present TeraAgent, a distributed simulation engine that allows scaling out the computation of one simulation to multiple servers. We identify and address server communication bottlenecks and implement solutions for serialization and delta encoding to accelerate and reduce data transfer. TeraAgent can simulate 500 billion agents and scales to 84096 CPU cores. BioDynaMo has been widely adopted, including a prize-winning radiotherapy simulation recognized as a top 10 breakthrough in physics in 2024.

We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modified transformer architecture that incorporates learned normalization and simultaneous set-wise attention to user history and ranked items. This architecture enables several breakthrough achievements, including: (1) the deprecation of most manually designed feature engineering, outperforming the prior state-of-the-art system using only few features (compared to hundreds in the baseline), (2) validation of the scaling law for ranking systems, showing improved performance with larger models, more training data, and longer context sequences, and (3) simultaneous joint scoring of items in a set-wise manner, leading to automated improvements in diversity. To enable efficient serving of large ranking models, we describe techniques to scale inference effectively using single-pass processing of user history and set-wise attention. We also summarize key insights from various ablation studies and A/B tests, highlighting the most impactful technical approaches.

Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a more extensive set of monitoring variables across multiple subsystems. However, learning causal graphs comes with a significant computational burden that restrains the applicability of most existing methods in real-time and large-scale deployments. In addition, modern monitoring applications for large systems often generate large amounts of binary alarm flags, and the distinct characteristics of binary anomaly data -- the meaning of state transition and data sparsity -- challenge existing causality learning mechanisms. This study proposes an anomaly causal discovery approach (AnomalyCD), addressing the accuracy and computational challenges of generating causal graphs from binary flag data sets. The AnomalyCD framework presents several strategies, such as anomaly flag characteristics incorporating causality testing, sparse data and link compression, and edge pruning adjustment approaches. We validate the performance of this framework on two datasets: monitoring sensor data of the readout-box system of the Compact Muon Solenoid experiment at CERN, and a public data set for information technology monitoring. The results demonstrate the considerable reduction of the computation overhead and moderate enhancement of the accuracy of temporal causal discovery on binary anomaly data sets.

Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, focusing on reducing biases in training data by utilizing generative large language models. The approach is evaluated in both cross-domain (irony detection) and cross-lingual (English) settings. Results show that the debiasing method enhances the robustness and generalizability of the models for satire and irony detection tasks in Turkish and English. However, its impact on causal language models, such as Llama-3.1, is limited. Additionally, this work curates and presents the Turkish Satirical News Dataset with detailed human annotations, with case studies on classification, debiasing, and explainability.

Large language models (LLMs) used across enterprises often use proprietary models and operate on sensitive inputs and data. The wide range of attack vectors identified in prior research - targeting various software and hardware components used in training and inference - makes it extremely challenging to enforce confidentiality and integrity policies. As we advance towards constructing compound AI inference pipelines that integrate multiple large language models (LLMs), the attack surfaces expand significantly. Attackers now focus on the AI algorithms as well as the software and hardware components associated with these systems. While current research often examines these elements in isolation, we find that combining cross-layer attack observations can enable powerful end-to-end attacks with minimal assumptions about the threat model. Given, the sheer number of existing attacks at each layer, we need a holistic and systemized understanding of different attack vectors at each layer. This SoK discusses different software and hardware attacks applicable to compound AI systems and demonstrates how combining multiple attack mechanisms can reduce the threat model assumptions required for an isolated attack. Next, we systematize the ML attacks in lines with the Mitre Att&ck framework to better position each attack based on the threat model. Finally, we outline the existing countermeasures for both software and hardware layers and discuss the necessity of a comprehensive defense strategy to enable the secure and high-performance deployment of compound AI systems.