pwc-archive/papers-with-abstracts huggingface dataset:
Monocular and Generalizable Gaussian Talking Head Animation
Authors: Shengjie Gong, Haojie Li, Jiapeng Tang, Dongming Hu, Shuangping Huang, Hao Chen, Tianshui Chen, Zhuoman Liu
Date: 2025-04-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Depth Estimation

In this work, we introduce Monocular and Generalizable Gaussian Talking Head Animation (MGGTalk), which requires monocular datasets and generalizes to unseen identities without personalized re-training. Compared with previous 3D Gaussian Splatting (3DGS) methods that requires elusive multi-view datasets or tedious personalized learning/inference, MGGtalk enables more practical and broader applications. However, in the absence of multi-view and personalized training data, the incompleteness of geometric and appearance information poses a significant challenge. To address these challenges, MGGTalk explores depth information to enhance geometric and facial symmetry characteristics to supplement both geometric and appearance features. Initially, based on the pixel-wise geometric information obtained from depth estimation, we incorporate symmetry operations and point cloud filtering techniques to ensure a complete and precise position parameter for 3DGS. Subsequently, we adopt a two-stage strategy with symmetric priors for predicting the remaining 3DGS parameters. We begin by predicting Gaussian parameters for the visible facial regions of the source image. These parameters are subsequently utilized to improve the prediction of Gaussian parameters for the non-visible regions. Extensive experiments demonstrate that MGGTalk surpasses previous state-of-the-art methods, achieving superior performance across various metrics.
Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction
Authors: Jing Chen, Shixiang Pan, Weimin Peng, Wenqiang Xu
Date: 2025-04-01 00:00:00
Proceeding: Neural Networks 2025 4
Tasks: Spatio-Temporal Forecasting, Time Series, Traffic Prediction

Accurate traffic flow forecasting is critical for intelligent transportation systems, yet increasing model complexity in spatiotemporal graph neural networks does not always yield proportional gains. In this paper, we present a Bilinear Spatiotemporal Fusion Network (BLSTF) tailored for stable, periodic traffic scenarios. First, a temporal enhancement module is introduced to mitigate multi-step error accumulation. Second, predefined graph priors with linear feedback leverage known road topologies for straightforward yet effective spatial modeling. Finally, a bilinear fusion mechanism seamlessly integrates refined temporal and spatial features with minimal computational overhead. Extensive experiments on four real-world datasets show that BLSTF outperforms state-of-the-art methods, achieving MAE and MAPE of 14.05 and 13.90% on PEMS03, 17.93 and 12.12% on PEMS04, 18.87 and 7.86% on PEMS07, and 13.49 and 8.71% on PEMS08, demonstrating BLSTF’s potential to deliver accurate, efficient, and interpretable traffic flow forecasts.
MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving
Authors: Zhiyuan Zhang, Xiaofan Li, Zhihao Xu, Wenjie Peng, Zijian Zhou, Miaojing Shi, Shuangping Huang
Date: 2025-04-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving, Prompt Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Autonomous driving visual question answering (AD-VQA) aims to answer questions related to perception, prediction, and planning based on given driving scene images, heavily relying on the model's spatial understanding capabilities. Prior works typically express spatial information through textual representations of coordinates, resulting in semantic gaps between visual coordinate representations and textual descriptions. This oversight hinders the accurate transmission of spatial information and increases the expressive burden. To address this, we propose a novel Marker-based Prompt learning framework (MPDrive), which represents spatial coordinates by concise visual markers, ensuring linguistic expressive consistency and enhancing the accuracy of both visual perception and spatial expression in AD-VQA. Specifically, we create marker images by employing a detection expert to overlay object regions with numerical labels, converting complex textual coordinate generation into straightforward text-based visual marker predictions. Moreover, we fuse original and marker images as scene-level features and integrate them with detection priors to derive instance-level features. By combining these features, we construct dual-granularity visual prompts that stimulate the LLM's spatial perception capabilities. Extensive experiments on the DriveLM and CODA-LM datasets show that MPDrive achieves state-of-the-art performance, particularly in cases requiring sophisticated spatial understanding.
Energy Efficient Learning Algorithms for Glaucoma Diagnosis
Authors: Krish Nachnani
Date: 2024-03-19 00:00:00
Proceeding: International Conference on Machine Learning and Applications (ICMLA) 2024 3

Glaucoma is a widespread issue that affects millions of individuals across the globe. A shortage of trained ophthalmologists is just one reason why broad screening is challenging, particularly in rural areas. To address this issue requires not just automation assistance but efficient assistance to match the limited resources in rural areas. This study evaluates energy efficient techniques to classify glaucoma -- techniques that can enable efficient automation assistance for physicians operating in limited environments. Two energy effective detection techniques (MobileNetV2 and machine learning algorithms over featurized data) are evaluated on the ORIGA dataset and the results are compared to variants of ResNet, a state-of-the-art convolutional neural network. While the featurization method exceeds ResNet's performance, MobileNetV2 falls short. This work provides a pathway for energy efficient implementations of glaucoma detection.
Trust-Region Twisted Policy Improvement
Authors: Joery A. de Vries, Jinke He, Yaniv Oren, Matthijs T. J. Spaan
Date: 2025-04-08 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Monte-Carlo tree search (MCTS) has driven many recent breakthroughs in deep reinforcement learning (RL). However, scaling MCTS to parallel compute has proven challenging in practice which has motivated alternative planners like sequential Monte-Carlo (SMC). Many of these SMC methods adopt particle filters for smoothing through a reformulation of RL as a policy inference problem. Yet, persisting design choices of these particle filters often conflict with the aim of online planning in RL, which is to obtain a policy improvement at the start of planning. Drawing inspiration from MCTS, we tailor SMC planners specifically for RL by improving data generation within the planner through constrained action sampling and explicit terminal state handling, as well as improving policy and value target estimation. This leads to our Trust-Region Twisted SMC (TRT-SMC), which shows improved runtime and sample-efficiency over baseline MCTS and SMC methods in both discrete and continuous domains.
GPU Performance Portability needs Autotuning
Authors: Burkhard Ringlein, Thomas Parnell, Radu Stoica
Date: 2025-04-30 00:00:00
Tasks: GPU

As LLMs grow in complexity, achieving state-of-the-art performance requires tight co-design across algorithms, software, and hardware. Today's reliance on a single dominant platform limits portability, creates vendor lock-in, and raises barriers for new AI hardware. In this work, we make the case for combining just-in-time (JIT) compilation with kernel parameter autotuning to enable portable LLM inference with state-of-the-art performance without code changes. Focusing on flash attention -- a widespread performance critical LLM kernel -- we demonstrate that this approach explores up to 15x more kernel parameter configurations, produces significantly more diverse code across multiple dimensions, and even outperforms vendor-optimized implementations by up to 230%, all while reducing kernel code size by 70x and eliminating manual code optimizations. Our results highlight autotuning as a promising path to unlocking model portability across GPU vendors.
Splitwiser: Efficient LM inference with constrained resources
Authors: Asad Aali, Adney Cardoza, Melissa Capo
Date: 2025-04-21 00:00:00
Tasks: GPU, Scheduling

Efficient inference of LLMs remains a crucial challenge, with two main phases: a compute-intensive prompt computation and a memory-intensive token generation. Despite existing batching and scheduling techniques, token generation phases fail to fully utilize compute resources, especially when compared to prompt computation phases. To address these challenges, we propose Splitwiser, a methodology that splits the two phases of an LLM inference request onto the same GPU, thereby reducing overhead and improving memory access and cache utilization. By eliminating the need to transfer data across devices, Splitwiser aims to minimize network-related overheads. In this report, we describe the basic structure of our proposed pipeline while sharing preliminary results and analysis. We implement our proposed multiprocessing design on two widely-used and independent LLM architectures: Huggingface and vLLM. We open-source our code for the respective implementations: 1) Huggingface (https://github.com/asad-aali/splitwiser), and 2) vLLM (https://github.com/adney11/vllm-sysml).
Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management
Authors: Hang Zhang, Jiuchen Shi, Yixiao Wang, Quan Chen, Yizhou Shan, Minyi Guo
Date: 2025-04-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Management

Multiple Low-Rank Adapters (Multi-LoRAs) are gaining popularity for task-specific Large Language Model (LLM) applications. For multi-LoRA serving, caching hot KV caches and LoRA adapters in high bandwidth memory of accelerations can improve inference performance. However, existing Multi-LoRA inference systems fail to optimize serving performance like Time-To-First-Toke (TTFT), neglecting usage dependencies when caching LoRAs and KVs. We therefore propose FASTLIBRA, a Multi-LoRA caching system to optimize the serving performance. FASTLIBRA comprises a dependency-aware cache manager and a performance-driven cache swapper. The cache manager maintains the usage dependencies between LoRAs and KV caches during the inference with a unified caching pool. The cache swapper determines the swap-in or out of LoRAs and KV caches based on a unified cost model, when the HBM is idle or busy, respectively. Experimental results show that ELORA reduces the TTFT by 63.4% on average, compared to state-of-the-art works.
APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-Design
Authors: Yonghao Tan, Pingcheng Dong, Yongkun Wu, Yu Liu, Xuejiao Liu, Peng Luo, Shih-Yang Liu, Xijie Huang, Dong Zhang, Luhong Liang, Kwang-Ting Cheng
Date: 2025-04-10 00:00:00
Tasks: Model Compression, Quantization

DNN accelerators, significantly advanced by model compression and specialized dataflow techniques, have marked considerable progress. However, the frequent access of high-precision partial sums (PSUMs) leads to excessive memory demands in architectures utilizing input/weight stationary dataflows. Traditional compression strategies have typically overlooked PSUM quantization, which may account for 69% of power consumption. This study introduces a novel Additive Partial Sum Quantization (APSQ) method, seamlessly integrating PSUM accumulation into the quantization framework. A grouping strategy that combines APSQ with PSUM quantization enhanced by a reconfigurable architecture is further proposed. The APSQ performs nearly lossless on NLP and CV tasks across BERT, Segformer, and EfficientViT models while compressing PSUMs to INT8. This leads to a notable reduction in energy costs by 28-87%. Extended experiments on LLaMA2-7B demonstrate the potential of APSQ for large language models. Code is available at https://github.com/Yonghao-Tan/APSQ.
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design
Authors: Yanbiao Liang, Huihong Shi, Haikuo Shao, Zhongfeng Wang
Date: 2025-04-07 00:00:00
Tasks: Quantization

Recently, large language models (LLMs) have achieved huge success in the natural language processing (NLP) field, driving a growing demand to extend their deployment from the cloud to edge devices. However, deploying LLMs on resource-constrained edge devices poses significant challenges, including (1) intensive computations and huge model sizes, (2) great memory and bandwidth demands introduced by the autoregressive generation process, and (3) limited scalability for handling long sequences. To address these challenges, we propose AccLLM, a comprehensive acceleration framework that enables efficient and fast long-context LLM inference through algorithm and hardware co-design. At the algorithmic level, we integrate (1) pruning, (2) {\Lambda}-shaped attention, and (3) an innovative W2A8KV4 (2-bit weights, 8-bit activations, and 4-bit KV cache) quantization scheme, thus effectively reducing memory and bandwidth requirements while facilitating LLMs' long-sequence generation. At the hardware level, we design a dedicated FPGA-based accelerator with a reconfigurable computing engine to effectively and flexibly accommodate diverse operations arising from our compression algorithm, thereby fully translating the algorithmic innovations into tangible hardware efficiency. We validate AccLLM on the Xilinx Alveo U280 FPGA, demonstrating a 4.07x energy efficiency and a 2.98x throughput compared to the state-of-the-art work FlightLLM.
MatMMFuse: Multi-Modal Fusion model for Material Property Prediction
Authors: Abhiroop Bhattacharya, Sylvain G. Cloutier
Date: 2025-04-30 00:00:00
Tasks: Band Gap, Formation Energy, Property Prediction

The recent progress of using graph based encoding of crystal structures for high throughput material property prediction has been quite successful. However, using a single modality model prevents us from exploiting the advantages of an enhanced features space by combining different representations. Specifically, pre-trained Large language models(LLMs) can encode a large amount of knowledge which is beneficial for training of models. Moreover, the graph encoder is able to learn the local features while the text encoder is able to learn global information such as space group and crystal symmetry. In this work, we propose Material Multi-Modal Fusion(MatMMFuse), a fusion based model which uses a multi-head attention mechanism for the combination of structure aware embedding from the Crystal Graph Convolution Network (CGCNN) and text embeddings from the SciBERT model. We train our model in an end-to-end framework using data from the Materials Project Dataset. We show that our proposed model shows an improvement compared to the vanilla CGCNN and SciBERT model for all four key properties: formation energy, band gap, energy above hull and fermi energy. Specifically, we observe an improvement of 40% compared to the vanilla CGCNN model and 68% compared to the SciBERT model for predicting the formation energy per atom. Importantly, we demonstrate the zero shot performance of the trained model on small curated datasets of Perovskites, Chalcogenides and the Jarvis Dataset. The results show that the proposed model exhibits better zero shot performance than the individual plain vanilla CGCNN and SciBERT model. This enables researchers to deploy the model for specialized industrial applications where collection of training data is prohibitively expensive.
A Comprehensive Analysis of Adversarial Attacks against Spam Filters
Authors: Esra Hotoğlu, Sevil Sen, Burcu Can
Date: 2025-05-04 00:00:00
Tasks: Deep Learning, Sentence, Spam detection

Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates the impact of adversarial attacks on deep learning-based spam detection systems using real-world datasets. Six prominent deep learning models are evaluated on these datasets, analyzing attacks at the word, character sentence, and AI-generated paragraph-levels. Novel scoring functions, including spam weights and attention weights, are introduced to improve attack effectiveness. This comprehensive analysis sheds light on the vulnerabilities of spam filters and contributes to efforts to improve their security against evolving adversarial threats.
Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning
Authors: Aditya Shinde, Prashant Doshi
Date: 2025-05-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning (IRL). Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attribution to known cyber adversary groups. Existing approaches rely on documenting an ever-evolving set of attacker tools and techniques to track known threat actors. Although attacks evolve constantly, attacker behavioral preferences are intrinsic and less volatile. Our approach learns the behavioral preferences of cyber adversaries from forensics data on their tools and techniques. We model the attacker as an expert decision-making agent with unknown behavioral preferences situated in a computer host. We leverage attack provenance graphs of audit logs to derive a state-action trajectory of the attack. We test our approach on open datasets of audit logs containing real attack data. Our results demonstrate for the first time that low-level forensics data can automatically reveal an adversary's subjective preferences, which serves as an additional dimension to modeling and documenting cyber adversaries. Attackers' preferences tend to be invariant despite their different tools and indicate predispositions that are inherent to the attacker. As such, these inferred preferences can potentially serve as unique behavioral signatures of attackers and improve threat attribution.
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection
Authors: Lokesh Koli, Shubham Kalra, Rohan Thakur, Anas Saifi, Karanpreet Singh
Date: 2025-05-01 00:00:00
Tasks: Anomaly Detection, Federated Learning, Management

Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management (IRM) system that integrates behavioral analytics, dynamic risk scoring, and real-time policy enforcement to detect and mitigate insider threats with high accuracy and adaptability. We introduce a hybrid scoring mechanism - transitioning from the static PRISM model to an adaptive AI-based model utilizing an autoencoder neural network trained on expert-annotated user activity data. Through iterative feedback loops and continuous learning, the system reduces false positives by 59% and improves true positive detection rates by 30%, demonstrating substantial gains in detection precision. Additionally, the platform scales efficiently, processing up to 10 million log events daily with sub-300ms query latency, and supports automated enforcement actions for policy violations, reducing manual intervention. The IRM system's deployment resulted in a 47% reduction in incident response times, highlighting its operational impact. Future enhancements include integrating explainable AI, federated learning, graph-based anomaly detection, and alignment with Zero Trust principles to further elevate its adaptability, transparency, and compliance-readiness. This work establishes a scalable and proactive framework for mitigating emerging insider risks in both on-premises and hybrid environments.
Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
Authors: Angelina Wang
Date: 2025-05-07 00:00:00
Tasks: Specificity

A key value proposition of machine learning is generalizability: the same methods and model architecture should be able to work across different domains and different contexts. While powerful, this generalization can sometimes go too far, and miss the importance of the specifics. In this work, we look at how fair machine learning has often treated as interchangeable the identity axis along which discrimination occurs. In other words, racism is measured and mitigated the same way as sexism, as ableism, as ageism. Disciplines outside of computer science have pointed out both the similarities and differences between these different forms of oppression, and in this work we draw out the implications for fair machine learning. While certainly not all aspects of fair machine learning need to be tailored to the specific form of oppression, there is a pressing need for greater attention to such specificity than is currently evident. Ultimately, context specificity can deepen our understanding of how to build more fair systems, widen our scope to include currently overlooked harms, and, almost paradoxically, also help to narrow our scope and counter the fear of an infinite number of group-specific methods of analysis.
Robust Online Learning with Private Information
Authors: Kyohei Okumura
Date: 2025-05-08 00:00:00

This paper investigates the robustness of online learning algorithms when learners possess private information. No-external-regret algorithms, prevalent in machine learning, are vulnerable to strategic manipulation, allowing an adaptive opponent to extract full surplus. Even standard no-weak-external-regret algorithms, designed for optimal learning in stationary environments, exhibit similar vulnerabilities. This raises a fundamental question: can a learner simultaneously prevent full surplus extraction by adaptive opponents while maintaining optimal performance in well-behaved environments? To address this, we model the problem as a two-player repeated game, where the learner with private information plays against the environment, facing ambiguity about the environment's types: stationary or adaptive. We introduce \emph{partial safety} as a key design criterion for online learning algorithms to prevent full surplus extraction. We then propose the \emph{Explore-Exploit-Punish} (\textsf{EEP}) algorithm and prove that it satisfies partial safety while achieving optimal learning in stationary environments, and has a variant that delivers improved welfare performance. Our findings highlight the risks of applying standard online learning algorithms in strategic settings with adverse selection. We advocate for a shift toward online learning algorithms that explicitly incorporate safeguards against strategic manipulation while ensuring strong learning performance.