pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models
Authors: Khawir Mahmood, Jehandad Khan, Hammad Afzal
Date: 2024-04-15 00:00:00
Tasks: GPU, Parameter Prediction

GPU kernels have come to the forefront of comput- ing due to their utility in varied fields, from high-performance computing to machine learning. A typical GPU compute kernel is invoked millions, if not billions of times in a typical application, which makes their performance highly critical. Due to the unknown nature of the optimization surface, an exhaustive search is required to discover the global optimum, which is infeasible due to the possible exponential number of parameter combinations. In this work, we propose a methodology that uses deep sequence- to-sequence models to predict the optimal tuning parameters governing compute kernels. This work considers the prediction of kernel parameters as a sequence to the sequence translation problem, borrowing models from the Natural Language Process- ing (NLP) domain. Parameters describing the input, output and weight tensors are considered as the input language to the model that emits the corresponding kernel parameters. In essence, the model translates the problem parameter language to kernel parameter language. The core contributions of this work are: a) Proposing that a sequence to sequence model can accurately learn the performance dynamics of a GPU compute kernel b) A novel network architecture which predicts the kernel tuning parameters for GPU kernels, c) A constrained beam search which incorporates the physical limits of the GPU hardware as well as other expert knowledge reducing the search space. The proposed algorithm can achieve more than 90% accuracy on various convolutional kernels in MIOpen, the AMD machine learning primitives library. As a result, the proposed technique can reduce the development time and compute resources required to tune unseen input configurations, resulting in shorter development cycles, reduced development costs, and better user experience.
Shaping Realities: Enhancing 3D Generative AI with Fabrication Constraints
Authors: Faraz Faruqi, Yingtao Tian, Vrushank Phadnis, Varun Jampani, Stefanie Mueller
Date: 2024-04-15 00:00:00
Tasks: 3D geometry

Generative AI tools are becoming more prevalent in 3D modeling, enabling users to manipulate or create new models with text or images as inputs. This makes it easier for users to rapidly customize and iterate on their 3D designs and explore new creative ideas. These methods focus on the aesthetic quality of the 3D models, refining them to look similar to the prompts provided by the user. However, when creating 3D models intended for fabrication, designers need to trade-off the aesthetic qualities of a 3D model with their intended physical properties. To be functional post-fabrication, 3D models have to satisfy structural constraints informed by physical principles. Currently, such requirements are not enforced by generative AI tools. This leads to the development of aesthetically appealing, but potentially non-functional 3D geometry, that would be hard to fabricate and use in the real world. This workshop paper highlights the limitations of generative AI tools in translating digital creations into the physical world and proposes new augmentations to generative AI tools for creating physically viable 3D models. We advocate for the development of tools that manipulate or generate 3D models by considering not only the aesthetic appearance but also using physical properties as constraints. This exploration seeks to bridge the gap between digital creativity and real-world applicability, extending the creative potential of generative AI into the tangible domain.
Language Model Cascades: Token-level uncertainty and beyond
Authors: Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar
Date: 2024-04-15 00:00:00
Tasks: Language Modeling, Language Modelling, model

Recent advances in language models (LMs) have led to significant improvements in quality on complex NLP tasks, but at the expense of increased inference costs. Cascading offers a simple strategy to achieve more favorable cost-quality tradeoffs: here, a small model is invoked for most "easy" instances, while a few "hard" instances are deferred to the large model. While the principles underpinning cascading are well-studied for classification tasks - with deferral based on predicted class uncertainty favored theoretically and practically - a similar understanding is lacking for generative LM tasks. In this work, we initiate a systematic study of deferral rules for LM cascades. We begin by examining the natural extension of predicted class uncertainty to generative LM tasks, namely, the predicted sequence uncertainty. We show that this measure suffers from the length bias problem, either over- or under-emphasizing outputs based on their lengths. This is because LMs produce a sequence of uncertainty values, one for each output token; and moreover, the number of output tokens is variable across examples. To mitigate this issue, we propose to exploit the richer token-level uncertainty information implicit in generative LMs. We argue that naive predicted sequence uncertainty corresponds to a simple aggregation of these uncertainties. By contrast, we show that incorporating token-level uncertainty through learned post-hoc deferral rules can significantly outperform such simple aggregation strategies, via experiments on a range of natural language benchmarks with FLAN-T5 models. We further show that incorporating embeddings from the smaller model and intermediate layers of the larger model can give an additional boost in the overall cost-quality tradeoff.
Using Long Short-term Memory (LSTM) to merge precipitation data over mountainous area in Sierra Nevada
Authors: Yihan Wang, Lujun Zhang
Date: 2024-04-15 00:00:00

Obtaining reliable precipitation estimation with high resolutions in time and space is of great importance to hydrological studies. However, accurately estimating precipitation is a challenging task over high mountainous complex terrain. The three widely used precipitation measurement approaches, namely rainfall gauge, precipitation radars, and satellite-based precipitation sensors, have their own pros and cons in producing reliable precipitation products over complex areas. One way to decrease the detection error probability and improve data reliability is precipitation data merging. With the rapid advancements in computational capabilities and the escalating volume and diversity of earth observational data, Deep Learning (DL) models have gained considerable attention in geoscience. In this study, a deep learning technique, namely Long Short-term Memory (LSTM), was employed to merge a radar-based and a satellite-based Global Precipitation Measurement (GPM) precipitation product Integrated Multi-Satellite Retrievals for GPM (IMERG) precipitation product at hourly scale. The merged results are compared with the widely used reanalysis precipitation product, Multi-Radar Multi-Sensor (MRMS), and assessed against gauge observational data from the California Data Exchange Center (CDEC). The findings indicated that the LSTM-based merged precipitation notably underestimated gauge observations and, at times, failed to provide meaningful estimates, showing predominantly near-zero values. Relying solely on individual Quantitative Precipitation Estimates (QPEs) without additional meteorological input proved insufficient for generating reliable merged QPE. However, the merged results effectively captured the temporal trends of the observations, outperforming MRMS in this aspect. This suggested that incorporating bias correction techniques could potentially enhance the accuracy of the merged product.
LegalPro-BERT: Classification of Legal Provisions by fine-tuning BERT Large Language Model
Authors: Amit Tewari
Date: 2024-04-15 00:00:00
Tasks: Language Modeling

A contract is a type of legal document commonly used in organizations. Contract review is an integral and repetitive process to avoid business risk and liability. Contract analysis requires the identification and classification of key provisions and paragraphs within an agreement. Identification and validation of contract clauses can be a time-consuming and challenging task demanding the services of trained and expensive lawyers, paralegals or other legal assistants. Classification of legal provisions in contracts using artificial intelligence and natural language processing is complex due to the requirement of domain-specialized legal language for model training and the scarcity of sufficient labeled data in the legal domain. Using general-purpose models is not effective in this context due to the use of specialized legal vocabulary in contracts which may not be recognized by a general model. To address this problem, we propose the use of a pre-trained large language model which is subsequently calibrated on legal taxonomy. We propose LegalPro-BERT, a BERT transformer architecture model that we fine- tune to efficiently handle classification task for legal provisions. We conducted experiments to measure and compare metrics with current benchmark results. We found that LegalPro-BERT outperforms the previous benchmark used for comparison in this research.
Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD)
Authors: Yiqiao Yin
Date: 2024-04-15 00:00:00
Tasks: Data Augmentation, Prediction

Recent advancements in sequence prediction have significantly improved the accuracy of video data interpretation; however, existing models often overlook the potential of attention-based mechanisms for next-frame prediction. This study introduces the Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD), an innovative approach that integrates attention mechanisms into sequence prediction, enabling nuanced analysis and understanding of temporal dynamics in video sequences. Utilizing the Moving MNIST dataset, we demonstrate VAPAAD's robust performance and superior handling of complex temporal data compared to traditional methods. VAPAAD combines data augmentation, ConvLSTM2D layers, and a custom-built self-attention mechanism to effectively focus on salient features within a sequence, enhancing predictive accuracy and context-aware analysis. This methodology not only adheres to human cognitive processes during video interpretation but also addresses limitations in conventional models, which often struggle with the variability inherent in video sequences. The experimental results confirm that VAPAAD outperforms existing models, especially in integrating attention mechanisms, which significantly improve predictive performance.
AIGeN: An Adversarial Approach for Instruction Generation in VLN
Authors: Niyati Rawal, Roberto Bigazzi, Lorenzo Baraldi, Rita Cucchiara
Date: 2024-04-15 00:00:00
Tasks: Decoder, Vision and Language Navigation

In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance.
Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks
Authors: Ammar Ahmed Pallikonda Latheef, Alberto Santamaria-Pang, Craig K Jones, Haris I Sair
Date: 2024-04-15 00:00:00

Brain networks display a hierarchical organization, a complexity that poses a challenge for existing deep learning models, often structured as flat classifiers, leading to difficulties in interpretability and the 'black box' issue. To bridge this gap, we propose a novel architecture: a symbolic autoencoder informed by weak supervision and an Emergent Language (EL) framework. This model moves beyond traditional flat classifiers by producing hierarchical clusters and corresponding imagery, subsequently represented through symbolic sentences to improve the clinical interpretability of hierarchically organized data such as intrinsic brain networks, which can be characterized using resting-state fMRI images. Our innovation includes a generalized hierarchical loss function designed to ensure that both sentences and images accurately reflect the hierarchical structure of functional brain networks. This enables us to model functional brain networks from a broader perspective down to more granular details. Furthermore, we introduce a quantitative method to assess the hierarchical consistency of these symbolic representations. Our qualitative analyses show that our model successfully generates hierarchically organized, clinically interpretable images, a finding supported by our quantitative evaluations. We find that our best performing loss function leads to a hierarchical consistency of over 97% when identifying images corresponding to brain networks. This approach not only advances the interpretability of deep learning models in neuroimaging analysis but also represents a significant step towards modeling the intricate hierarchical nature of brain networks.
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Authors: Yogesh Verma, Markus Heinonen, Vikas Garg
Date: 2024-04-15 00:00:00
Tasks: Uncertainty Quantification, Weather Forecasting

Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenged the simulation paradigm with complex network forecasts. However, they often act as data-driven black-box models that neglect the underlying physics and lack uncertainty quantification. We address these limitations with ClimODE, a spatiotemporal continuous-time process that implements a key principle of advection from statistical mechanics, namely, weather changes due to a spatial movement of quantities over time. ClimODE models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow, which also enables estimating the uncertainty in predictions. Our approach outperforms existing data-driven methods in global and regional forecasting with an order of magnitude smaller parameterization, establishing a new state of the art.
Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data
Authors: Yu Wang, Shu-Rui Zhang, Aidin Momtaz, Rahim Moradi, Fatemeh Rastegarnia, Narek Sahakyan, Soroush Shakeri, Liang Li
Date: 2024-04-14 00:00:00

ChatGPT has been the most talked-about concept in recent months, captivating both professionals and the general public alike, and has sparked discussions about the changes that artificial intelligence (AI) will bring to the world. As physicists and astrophysicists, we are curious about if scientific data can be correctly analyzed by large language models (LLMs) and yield accurate physics. In this article, we fine-tune the generative pre-trained transformer (GPT) model by the astronomical data from the observations of galaxies, quasars, stars, gamma-ray bursts (GRBs), and the simulations of black holes (BHs), the fine-tuned model demonstrates its capability to classify astrophysical phenomena, distinguish between two types of GRBs, deduce the redshift of quasars, and estimate BH parameters. We regard this as a successful test, marking the LLM's proven efficacy in scientific research. With the ever-growing volume of multidisciplinary data and the advancement of AI technology, we look forward to the emergence of a more fundamental and comprehensive understanding of our universe. This article also shares some interesting thoughts on data collection and AI design. Using the approach of understanding the universe - looking outward at data and inward for fundamental building blocks - as a guideline, we propose a method of series expansion for AI, suggesting ways to train and control AI that is smarter than humans.
VideoSAGE: Video Summarization with Graph Representation Learning
Authors: Jose M. Rojas Chaves, Subarna Tripathi
Date: 2024-04-14 00:00:00
Tasks: Graph Representation Learning, Node Classification, Representation Learning, Video Summarization

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a binary node classification problem, precisely classifying video frames whether they should belong to the output summary video. A graph constructed this way aims to capture long-range interactions among video frames, and the sparsity ensures the model trains without hitting the memory and compute bottleneck. Experiments on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed nimble model compared to existing state-of-the-art summarization approaches while being one order of magnitude more efficient in compute time and memory
Model-based Offline Quantum Reinforcement Learning
Authors: Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler
Date: 2024-04-14 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning

This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function. This model-based approach allows, in principle, full realization on a quantum computer during the optimization phase and gives hope that a quantum advantage can be achieved as soon as sufficiently powerful quantum computers are available.
A biologically inspired computational trust model for open multi-agent systems which is resilient to trustor population changes
Authors: Zoi Lygizou, Dimitris Kalles
Date: 2024-04-13 00:00:00

Current trust and reputation models continue to have significant limitations, such as the inability to deal with agents constantly entering or exiting open multi-agent systems (open MAS), as well as continuously changing behaviors. Our study is based on CA, a previously proposed decentralized computational trust model from the trustee's point of view, inspired by synaptic plasticity and the formation of assemblies in the human brain. It is designed to meet the requirements of highly dynamic and open MAS, and its main difference with most conventional trust and reputation models is that the trustor does not select a trustee to delegate a task; instead, the trustee determines whether it is qualified to successfully execute it. We ran a series of simulations to compare CA model to FIRE, a well-established, decentralized trust and reputation model for open MAS under conditions of continuous trustee and trustor population replacement, as well as continuous change of trustees' abilities to perform tasks. The main finding is that FIRE is superior to changes in the trustee population, whereas CA is resilient to the trustor population changes. When the trustees switch performance profiles FIRE clearly outperforms despite the fact that both models' performances are significantly impacted by this environmental change. Findings lead us to conclude that learning to use the appropriate trust model, according to the dynamic conditions in effect could maximize the trustor's benefits.
Into the Fog: Evaluating Robustness of Multiple Object Tracking
Authors: Nadezda Kirillova, M. Jehanzeb Mirza, Horst Bischof, Horst Possegger
Date: 2024-04-12 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Multiple Object Tracking, Object, Object Tracking

State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overlooking adverse atmospheric conditions such as fog, haze, smoke and dust. As a result, the robustness of trackers against these challenging conditions remains underexplored. To address this gap, we introduce physics-based volumetric fog simulation method for arbitrary MOT datasets, utilizing frame-by-frame monocular depth estimation and a fog formation optical model. We enhance our simulation by rendering both homogeneous and heterogeneous fog and propose to use the dark channel prior method to estimate atmospheric light, showing promising results even in night and indoor scenes. We present the leading benchmark MOTChallenge (third release) augmented with fog (smoke for indoor scenes) of various intensities and conduct a comprehensive evaluation of MOT methods, revealing their limitations under fog and fog-like challenges.
Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes
Authors: Zehao Yu, Torsten Sattler, Andreas Geiger
Date: 2024-04-16 00:00:00
Tasks: 3DGS, Novel View Synthesis, Surface Reconstruction

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset, without resorting to Poisson reconstruction or TSDF fusion as in previous work. We approximate the surface normal of Gaussians as the normal of the ray-Gaussian intersection plane, enabling the application of regularization that significantly enhances geometry. Furthermore, we develop an efficient geometry extraction method utilizing Marching Tetrahedra, where the tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis. Further, it compares favorably to or even outperforms, neural implicit methods in both quality and speed.