pwc-archive/papers-with-abstracts huggingface dataset:
Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
Authors: Chao Fang, Man Shi, Robin Geens, Arne Symons, Zhongfeng Wang, Marian Verhelst
Date: 2024-11-24 00:00:00
Tasks: GPU

The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage requirements while maintaining accuracy. However, this shifts the energy and latency bottlenecks towards the FP activations that are associated with costly memory accesses and computations. Existing LLM accelerators focus primarily on computation optimizations, overlooking the potential of jointly optimizing FP computations and data movement, particularly for the dominant FP-INT GeMM operations in LLM inference. To address these challenges, we investigate the sensitivity of activation precision across various LLM modules and its impact on overall model accuracy. Based on our findings, we first propose the Anda data type: an adaptive data format with group-shared exponent bits and dynamic mantissa bit allocation. Secondly, we develop an iterative post-training adaptive precision search algorithm that optimizes the bit-width for different LLM modules to balance model accuracy, energy efficiency, and inference speed. Lastly, a suite of hardware optimization techniques is proposed to maximally exploit the benefits of the Anda format. These include a bit-plane-based data organization scheme, Anda-enhanced processing units with bit-serial computation, and a runtime bit-plane Anda compressor to simultaneously optimize storage, computation, and memory footprints. Our evaluations on FPINT GeMM operations show that Anda achieves a 2.4x speedup, 4.0x area efficiency, and 3.1x energy efficiency improvement on average for popular LLMs including OPT, LLaMA, and LLaMA-2 series over the GPU-like FP-FP baseline. Anda demonstrates strong adaptability across various application scenarios, accuracy requirements, and system performance, enabling efficient LLM inference across a wide range of deployment scenarios.
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation
Authors: Ruiqiang Xiao, Songning Lai, Yijun Yang, Jiemin Wu, Yutao Yue, Lei Zhu
Date: 2024-11-24 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Unsupervised Domain Adaptation

Adapting machine learning models to new domains without labeled data, especially when source data is inaccessible, is a critical challenge in applications like medical imaging, autonomous driving, and remote sensing. This task, known as Source-Free Unsupervised Domain Adaptation (SFUDA), involves adapting a pre-trained model to a target domain using only unlabeled target data, which can lead to issues such as overfitting, underfitting, and poor generalization due to domain discrepancies and noise. Existing SFUDA methods often rely on single-model architectures, struggling with uncertainty and variability in the target domain. To address these challenges, we propose DRIVE (Dual-Robustness through Information Variability and Entropy), a novel SFUDA framework leveraging a dual-model architecture. The two models, initialized with identical weights, work in parallel to capture diverse target domain characteristics. One model is exposed to perturbations via projection gradient descent (PGD) guided by mutual information, focusing on high-uncertainty regions. We also introduce an entropy-aware pseudo-labeling strategy that adjusts label weights based on prediction uncertainty, ensuring the model focuses on reliable data while avoiding noisy regions. The adaptation process has two stages: the first aligns the models on stable features using a mutual information consistency loss, and the second dynamically adjusts the perturbation level based on the loss from the first stage, encouraging the model to explore a broader range of the target domain while preserving existing performance. This enhances generalization capabilities and robustness against interference. Evaluations on standard SFUDA benchmarks show that DRIVE consistently outperforms previous methods, delivering improved adaptation accuracy and stability across complex target domains.
Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation
Authors: Chiranjeevi Bura, Praveen Kumar Myakala
Date: 2024-11-24 00:00:00

Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement. This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps. Ethical considerations such as algorithmic bias, data privacy, and AI role in human centric education are emphasized. The findings underscore the need for responsible AI integration that ensures accessibility, equity, and innovation in educational systems.
Partial Identifiability and Misspecification in Inverse Reinforcement Learning
Authors: Joar Skalse, Alessandro Abate
Date: 2024-11-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function $R$ from a policy $\pi$. This problem is difficult, for several reasons. First of all, there are typically multiple reward functions which are compatible with a given policy; this means that the reward function is only *partially identifiable*, and that IRL contains a certain fundamental degree of ambiguity. Secondly, in order to infer $R$ from $\pi$, an IRL algorithm must have a *behavioural model* of how $\pi$ relates to $R$. However, the true relationship between human preferences and human behaviour is very complex, and practically impossible to fully capture with a simple model. This means that the behavioural model in practice will be *misspecified*, which raises the worry that it might lead to unsound inferences if applied to real-world data. In this paper, we provide a comprehensive mathematical analysis of partial identifiability and misspecification in IRL. Specifically, we fully characterise and quantify the ambiguity of the reward function for all of the behavioural models that are most common in the current IRL literature. We also provide necessary and sufficient conditions that describe precisely how the observed demonstrator policy may differ from each of the standard behavioural models before that model leads to faulty inferences about the reward function $R$. In addition to this, we introduce a cohesive framework for reasoning about partial identifiability and misspecification in IRL, together with several formal tools that can be used to easily derive the partial identifiability and misspecification robustness of new IRL models, or analyse other kinds of reward learning algorithms.
Generative Prompt Internalization
Authors: Haebin Shin, Lei Ji, Yeyun Gong, Sungdong Kim, Eunbi Choi, Minjoon Seo
Date: 2024-11-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Prompts used in recent large language model based applications are often fixed and lengthy, leading to significant computational overhead. To address this challenge, we propose Generative Prompt Internalization (GenPI), a lightweight method that employs a joint training approach. GenPI not only replicates the behavior of models with prompt inputs but also generates the content of the prompt along with reasons for why the model's behavior should change accordingly. We demonstrate that our approach effectively internalizes complex prompts across various agent-based application scenarios. For effective training without interactions with the dedicated environments, we introduce a data synthesis technique that autonomously collects conversational datasets by swapping the roles of the agent and environment. This method is especially useful in scenarios where only a predefined prompt is available without a corresponding training dataset. By internalizing complex prompts, Generative Prompt Internalization enables high performance and efficient inference without the need for explicit prompts.
Making Images from Images: Interleaving Denoising and Transformation
Authors: Shumeet Baluja, David Marwood, Ashwin Baluja
Date: 2024-11-24 00:00:00
Tasks: Denoising

Simply by rearranging the regions of an image, we can create a new image of any subject matter. The definition of regions is user definable, ranging from regularly and irregularly-shaped blocks, concentric rings, or even individual pixels. Our method extends and improves recent work in the generation of optical illusions by simultaneously learning not only the content of the images, but also the parameterized transformations required to transform the desired images into each other. By learning the image transforms, we allow any source image to be pre-specified; any existing image (e.g. the Mona Lisa) can be transformed to a novel subject. We formulate this process as a constrained optimization problem and address it through interleaving the steps of image diffusion with an energy minimization step. Unlike previous methods, increasing the number of regions actually makes the problem easier and improves results. We demonstrate our approach in both pixel and latent spaces. Creative extensions, such as using infinite copies of the source image and employing multiple source images, are also given.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan
Authors: Saba Zahid, Sajid Ghuffar, Obaid-ur-Rehman, Syed Roshaan Ali Shah
Date: 2024-11-24 00:00:00
Tasks: Field Boundary Delineation, Semantic Segmentation, Transfer Learning

This study explores the effectiveness of multi-temporal satellite imagery for better functional field boundary delineation using deep learning semantic segmentation architecture on two distinct geographical and multi-scale farming systems of Netherlands and Pakistan. Multidate images of April, August and October 2022 were acquired for PlanetScope and Sentinel-2 in sub regions of Netherlands and November 2022, February and March 2023 for selected area of Dunyapur in Pakistan. For Netherlands, Basic registration crop parcels (BRP) vector layer was used as labeled training data. while self-crafted field boundary vector data were utilized for Pakistan. Four deep learning models with UNET architecture were evaluated using different combinations of multi-date images and NDVI stacks in the Netherlands subregions. A comparative analysis of IoU scores assessed the effectiveness of the proposed multi-date NDVI stack approach. These findings were then applied for transfer learning, using pre-trained models from the Netherlands on the selected area in Pakistan. Additionally, separate models were trained using self-crafted field boundary data for Pakistan, and combined models were developed using data from both the Netherlands and Pakistan. Results indicate that multi-date NDVI stacks provide additional temporal context, reflecting crop growth over different times of the season. The study underscores the critical role of multi-scale ground information from diverse geographical areas in developing robust and universally applicable models for field boundary delineation. The results also highlight the importance of fine spatial resolution for extraction of field boundaries in regions with small scale framing. The findings can be extended to multi-scale implementations for improved automatic field boundary delineation in heterogeneous agricultural environments.
A Training-Free Approach for Music Style Transfer with Latent Diffusion Models
Authors: Sooyoung Kim, Joonwoo Kwon, Heehwan Wang, Shinjae Yoo, Yuewei Lin, Jiook Cha
Date: 2024-11-24 00:00:00
Tasks: Music Generation, Music Style Transfer, Style Transfer

Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions. This paper introduces a novel training-free approach leveraging pre-trained Latent Diffusion Models (LDMs). By manipulating the self-attention features of the LDM, we effectively transfer the style of reference music onto content music without additional training. Our method achieves superior style transfer and melody preservation compared to existing methods. This work opens new creative avenues for personalized music generation.
Bimanual Grasp Synthesis for Dexterous Robot Hands
Authors: Yanming Shao, Chenxi Xiao
Date: 2024-11-24 00:00:00

Humans naturally perform bimanual skills to handle large and heavy objects. To enhance robots' object manipulation capabilities, generating effective bimanual grasp poses is essential. Nevertheless, bimanual grasp synthesis for dexterous hand manipulators remains underexplored. To bridge this gap, we propose the BimanGrasp algorithm for synthesizing bimanual grasps on 3D objects. The BimanGrasp algorithm generates grasp poses by optimizing an energy function that considers grasp stability and feasibility. Furthermore, the synthesized grasps are verified using the Isaac Gym physics simulation engine. These verified grasp poses form the BimanGrasp-Dataset, the first large-scale synthesized bimanual dexterous hand grasp pose dataset to our knowledge. The dataset comprises over 150k verified grasps on 900 objects, facilitating the synthesis of bimanual grasps through a data-driven approach. Last, we propose BimanGrasp-DDPM, a diffusion model trained on the BimanGrasp-Dataset. This model achieved a grasp synthesis success rate of 69.87\% and significant acceleration in computational speed compared to BimanGrasp algorithm.
Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos
Authors: C. Xiao, W. An, Y. Zhang, Z. Su, M. Li, W. Sheng, M. Pietikäinen, L. Liu
Date: 2024-11-24 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computational redundancy due to the severe imbalance between foreground and background regions. In this paper, we propose a highly efficient unsupervised framework for SVMOD. Specifically, we propose a generic unsupervised framework for SVMOD, in which pseudo labels generated by a traditional method can evolve with the training process to promote detection performance. Furthermore, we propose a highly efficient and effective sparse convolutional anchor-free detection network by sampling the dense multi-frame image form into a sparse spatio-temporal point cloud representation and skipping the redundant computation on background regions. Coping these two designs, we can achieve both high efficiency (label and computation efficiency) and effectiveness. Extensive experiments demonstrate that our method can not only process 98.8 frames per second on 1024x1024 images but also achieve state-of-the-art performance. The relabeled dataset and code are available at https://github.com/ChaoXiao12/Moving-object-detection-in-satellite-videos-HiEUM.
Navigating the Effect of Parametrization for Dimensionality Reduction
Authors: Haiyang Huang, Yingfan Wang, Cynthia Rudin
Date: 2024-11-24 00:00:00
Tasks: Dimensionality Reduction

Parametric dimensionality reduction methods have gained prominence for their ability to generalize to unseen datasets, an advantage that traditional approaches typically lack. Despite their growing popularity, there remains a prevalent misconception among practitioners about the equivalence in performance between parametric and non-parametric methods. Here, we show that these methods are not equivalent -- parametric methods retain global structure but lose significant local details. To explain this, we provide evidence that parameterized approaches lack the ability to repulse negative pairs, and the choice of loss function also has an impact. Addressing these issues, we developed a new parametric method, ParamRepulsor, that incorporates Hard Negative Mining and a loss function that applies a strong repulsive force. This new method achieves state-of-the-art performance on local structure preservation for parametric methods without sacrificing the fidelity of global structural representation. Our code is available at https://github.com/hyhuang00/ParamRepulsor.
Distribution-aware Online Continual Learning for Urban Spatio-Temporal Forecasting
Authors: Chengxin Wang, Gary Tan, Swagato Barman Roy, Beng Chin Ooi
Date: 2024-11-24 00:00:00
Tasks: Continual Learning, Scheduling, Spatio-Temporal Forecasting

Urban spatio-temporal (ST) forecasting is crucial for various urban applications such as intelligent scheduling and trip planning. Previous studies focus on modeling ST correlations among urban locations in offline settings, which often neglect the non-stationary nature of urban ST data, particularly, distribution shifts over time. This oversight can lead to degraded performance in real-world scenarios. In this paper, we first analyze the distribution shifts in urban ST data, and then introduce DOST, a novel online continual learning framework tailored for ST data characteristics. DOST employs an adaptive ST network equipped with a variable-independent adapter to address the unique distribution shifts at each urban location dynamically. Further, to accommodate the gradual nature of these shifts, we also develop an awake-hibernate learning strategy that intermittently fine-tunes the adapter during the online phase to reduce computational overhead. This strategy integrates a streaming memory update mechanism designed for urban ST sequential data, enabling effective network adaptation to new patterns while preventing catastrophic forgetting. Experimental results confirm DOST's superiority over state-of-the-art models on four real-world datasets, providing online forecasts within an average of 0.1 seconds and achieving a 12.89% reduction in forecast errors compared to baseline models.
Do LLMs Really Think Step-by-step In Implicit Reasoning?
Authors: Yijiong Yu
Date: 2024-11-24 00:00:00

It has been well-known that Chain-of-Thought can remarkably enhance LLMs' performance on complex tasks. However, because it also introduces slower inference speeds and higher computational costs, many researches have attempted to use implicit CoT, which does not need LLMs to explicitly generate the intermediate steps. However, the invisible reasoning process leaves us a doubt that, can implicit CoT really be equal to explicit CoT? Therefore, in this study, we address this question through experiments. We probe the information of intermediate steps from the model's hidden states when it is either trained or prompted to perform implicit CoT. The results surprisingly indicate that when prompted, LLMs hardly think about intermediate steps, suggesting they may just rely on experience rather than strict step-by-step reasoning. But when trained, they indeed calculate intermediate steps. Moreover, in both situations, we find the effect of using implicit CoT is susceptible to the format of the problem, reaffirming the current deficiency of implicit CoT.
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation
Authors: Fan Wang, Zhongyi Han, Xingbo Liu, Xin Gao, Yilong Yin
Date: 2024-11-24 00:00:00
Tasks: Domain Adaptation, Source-Free Domain Adaptation, Unsupervised Domain Adaptation

In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA), which leverages pre-trained source models without accessing source data. Evaluating the superiority of UDA versus SFDA is an open and timely question with significant implications for deploying adaptive algorithms in practical applications. In this study, we demonstrate through predictive coding theory and extensive experiments on multiple benchmark datasets that SFDA generally outperforms UDA in real-world scenarios. Specifically, SFDA offers advantages in time efficiency, storage requirements, targeted learning objectives, reduced risk of negative transfer, and increased robustness against overfitting. Notably, SFDA is particularly effective in mitigating negative transfer when there are substantial distribution discrepancies between source and target domains. Additionally, we introduce a novel data-model fusion scenario, where data sharing among stakeholders varies (e.g., some provide raw data while others provide only models), and reveal that traditional UDA and SFDA methods do not fully exploit their potential in this context. To address this limitation and capitalize on the strengths of SFDA, we propose a novel weight estimation method that effectively integrates available source data into multi-SFDA (MSFDA) approaches, thereby enhancing model performance within this scenario. This work provides a thorough analysis of UDA versus SFDA and advances a practical approach to model adaptation across diverse real-world environments.
Creating Scalable AGI: the Open General Intelligence Framework
Authors: Daniel A. Dollinger, Michael Singleton
Date: 2024-11-24 00:00:00
Tasks: image-classification, Image Classification, Medical Diagnosis

Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures, which are designed to handle only one data modality (data type) at a time. This single modal approach hinders their ability to integrate the complex set of data points required for real-world challenges and problem-solving tasks like medical diagnosis, quality assurance, equipment troubleshooting, and financial decision-making. Addressing these real-world challenges requires a more capable Artificial General Intelligence (AGI) system. Our primary contribution is the development of the Open General Intelligence (OGI) framework, a novel systems architecture that serves as a macro design reference for AGI. The OGI framework adopts a modular approach to the design of intelligent systems, based on the premise that cognition must occur across multiple specialized modules that can seamlessly operate as a single system. OGI integrates these modules using a dynamic processing system and a fabric interconnect, enabling real-time adaptability, multi-modal integration, and scalable processing. The OGI framework consists of three key components: (1) Overall Macro Design Guidance that directs operational design and processing, (2) a Dynamic Processing System that controls routing, primary goals, instructions, and weighting, and (3) Framework Areas, a set of specialized modules that operate cohesively to form a unified cognitive system. By incorporating known principles from human cognition into AI systems, the OGI framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.