OpenRaiser/Intern-Atlas huggingface dataset:
Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization
Venue: arXiv cs.CL 2024
Year: 2024

Fusing knowledge from multiple Large Language Models (LLMs) can combine their diverse strengths to achieve improved performance on a given task. However, current fusion approaches either rely on learning-based fusers that do not generalize to new LLMs, or do not take into account how well each LLM understands the input. In this work, we study LLM fusion at test-time, which enables leveraging knowledge from arbitrary user-specified LLMs during inference. We introduce Pack of LLMs (PackLLM), an effective method for test-time fusion that leverages each LLM's expertise, given an input prompt. PackLLM performs model fusion by solving an optimization problem for determining each LLM's importance, so that perplexity over the input prompt is minimized. First, our simple PackLLM-sim variant validates that perplexity is a good indicator for measuring each LLM's expertise. Second, our PackLLM-opt variant approximately solves the perplexity minimization problem via a greedy algorithm. The derived importance weights are used to combine the LLMs during inference. We conduct experiments with over 100 total LLMs on a diverse set of tasks. Experimental results show that (i) perplexity is a reliable measure for LLM fusion, (ii) PackLLM outperforms test-time fusion baselines by 1.89% accuracy points, and (iii) PackLLM can leverage new LLMs to improve performance over learning-based fusion approaches by 3.92-11.94% accuracy points.
Predicting Long-horizon Futures by Conditioning on Geometry and Time
Venue: arXiv cs.CV 2024
Year: 2024

Our work explores the task of generating future sensor observations conditioned on the past. We are motivated by `predictive coding' concepts from neuroscience as well as robotic applications such as self-driving vehicles. Predictive video modeling is challenging because the future may be multi-modal and learning at scale remains computationally expensive for video processing. To address both challenges, our key insight is to leverage the large-scale pretraining of image diffusion models which can handle multi-modality. We repurpose image models for video prediction by conditioning on new frame timestamps. Such models can be trained with videos of both static and dynamic scenes. To allow them to be trained with modestly-sized datasets, we introduce invariances by factoring out illumination and texture by forcing the model to predict (pseudo) depth, readily obtained for in-the-wild videos via off-the-shelf monocular depth networks. In fact, we show that simply modifying networks to predict grayscale pixels already improves the accuracy of video prediction. Given the extra controllability with timestamp conditioning, we propose sampling schedules that work better than the traditional autoregressive and hierarchical sampling strategies. Motivated by probabilistic metrics from the object forecasting literature, we create a benchmark for video prediction on a diverse set of videos spanning indoor and outdoor scenes and a large vocabulary of objects. Our experiments illustrate the effectiveness of learning to condition on timestamps, and show the importance of predicting the future with invariant modalities.
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation
Venue: arXiv cs.CV 2024
Year: 2024

We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts mechanism utilized in large language models (LLMs), MoA distributes the generation workload between two attention pathways: a personalized branch and a non-personalized prior branch. MoA is designed to retain the original model's prior by fixing its attention layers in the prior branch, while minimally intervening in the generation process with the personalized branch that learns to embed subjects in the layout and context generated by the prior branch. A novel routing mechanism manages the distribution of pixels in each layer across these branches to optimize the blend of personalized and generic content creation. Once trained, MoA facilitates the creation of high-quality, personalized images featuring multiple subjects with compositions and interactions as diverse as those generated by the original model. Crucially, MoA enhances the distinction between the model's pre-existing capability and the newly augmented personalized intervention, thereby offering a more disentangled subject-context control that was previously unattainable. Project page: https://snap-research.github.io/mixture-of-attention
Simple Image Signal Processing using Global Context Guidance
Venue: arXiv cs.CV 2024
Year: 2024

In modern smartphone cameras, the Image Signal Processor (ISP) is the core element that converts the RAW readings from the sensor into perceptually pleasant RGB images for the end users. The ISP is typically proprietary and handcrafted and consists of several blocks such as white balance, color correction, and tone mapping. Deep learning-based ISPs aim to transform RAW images into DSLR-like RGB images using deep neural networks. However, most learned ISPs are trained using patches (small regions) due to computational limitations. Such methods lack global context, which limits their efficacy on full-resolution images and harms their ability to capture global properties such as color constancy or illumination. First, we propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images. Second, we propose an efficient and simple neural ISP that utilizes our proposed module. Our model achieves state-of-the-art results on different benchmarks using diverse and real smartphone images.
The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
Venue: arXiv cs.AI 2024
Year: 2024

This survey paper examines the recent advancements in AI agent implementations, with a focus on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities. The primary objectives of this work are to a) communicate the current capabilities and limitations of existing AI agent implementations, b) share insights gained from our observations of these systems in action, and c) suggest important considerations for future developments in AI agent design. We achieve this by providing overviews of single-agent and multi-agent architectures, identifying key patterns and divergences in design choices, and evaluating their overall impact on accomplishing a provided goal. Our contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.
Functionality Locality, Mixture & Control = Logic = Memory
Venue: arXiv cs.AR 2024
Year: 2024

This work provides new insights and constructs to the field of computer architecture and systems, and these insights are expected to be useful for the broad software stack. First, this work introduces Functionality Locality: this form of Functionality Locality shows that functionalities can be changed with a single piece of information, by solely changing the access order. This broadens the scope of ``principle of locality", which originally includes spatial and temporal locality. Second, this work coins the term Mixture, by incorporating the layout-directed functionalities with the original quantifiers such as scalar and vector. The implications of Mixture significantly expands new understanding of quantifiers, and this work identifies several important ones (from the author perspective). Third, with Functionality and Mixture, this work identifies the principle ``Control = Logic = Memory", and provides a revisit to Von Neumann architectures and Harvard architectures. This centers the focus on the memory, and brings further guidelines on memory-centric architectures with a new analytic framework. Fourth, this work discusses several important implications from this work in a variety of aspects.
Learning with 3D rotations, a hitchhiker's guide to SO(3)
Venue: arXiv cs.LG 2024
Year: 2024

Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. This paper acts as a survey and guide through rotation representations. We walk through their properties that harm or benefit deep learning with gradient-based optimization. By consolidating insights from rotation-based learning, we provide a comprehensive overview of learning functions with rotation representations. We provide guidance on selecting representations based on whether rotations are in the model's input or output and whether the data primarily comprises small angles.
Simulating Cloud Environments of Connected Vehicles for Anomaly Detection
Venue: arXiv cs.ET 2024
Year: 2024

The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. When new software is deployed however, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior. Therefore, a concept is developed mirroring the existing cloud system into a simulation. To collect the necessary data, an observability platform is presented, capturing telemetry and architecture information. Subsequently, a simulation environment is designed that converts the architecture into a simulation model and simulates its dynamic workload by utilizing captured communication data. The proposed concept is evaluated in a real-world application scenario for electric vehicle charging: Vehicles can apply for an unoccupied charging station at a cloud service backend, the latter which manages all incoming requests and performs the assignment. Benchmarks are conducted by comparing the collected telemetry data with the simulated results under different loads and injected faults. The results show that regular cloud behavior is mirrored well by the simulation and that misbehavior due to fault injection is well visible, indicating that simulations are a promising data source for anomaly detection in connected vehicle cloud environments during operation.
IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery
Venue: arXiv cs.CV 2024
Year: 2024

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. This complexity is further compounded by the nature of Landsat's remote sensing data, where each pixel is rich with densely packed information, complicating the task of accurate irrigation mapping. In this study, we introduce an innovative approach that employs a progressive training method, which strategically increases patch sizes throughout the training process, utilizing datasets from Landsat 5 and 7, labeled with the WRLU dataset for precise labeling. This initial focus allows the model to capture detailed features, progressively shifting to broader, more general features as the patch size enlarges. Remarkably, our method enhances the performance of existing state-of-the-art models by approximately 20%. Furthermore, our analysis delves into the significance of incorporating various spectral bands into the model, assessing their impact on performance. The findings reveal that additional bands are instrumental in enabling the model to discern finer details more effectively. This work sets a new standard for leveraging remote sensing imagery in irrigation mapping.
QGen: On the Ability to Generalize in Quantization Aware Training
Venue: arXiv cs.LG 2024
Year: 2024

Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we develop a theoretical model for quantization in neural networks and demonstrate how quantization functions as a form of regularization. Second, motivated by recent work connecting the sharpness of the loss landscape and generalization, we derive an approximate bound for the generalization of quantized models conditioned on the amount of quantization noise. We then validate our hypothesis by experimenting with over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets on convolutional and transformer-based models.
Event-Based Eye Tracking. AIS 2024 Challenge Survey
Venue: arXiv cs.CV 2024
Year: 2024

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.
3D object quality prediction for Metal Jet Printer with Multimodal thermal encoder
Venue: arXiv cs.LG 2024
Year: 2024

With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifications. Various factors during metal printing affect the printed parts' quality, including the power quality, the printing stage parameters, the print part's location inside the print bed, the curing stage parameters, and the metal sintering process. With the large data gathered from HP's MetJet printing process, AI techniques can be used to analyze, learn, and effectively infer the printed part quality metrics, as well as assist in improving the print yield. In-situ thermal sensing data captured by printer-installed thermal sensors contains the part thermal signature of fusing layers. Such part thermal signature contains a convoluted impact from various factors. In this paper, we use a multimodal thermal encoder network to fuse data of a different nature including the video data vectorized printer control data, and exact part thermal signatures with a trained encoder-decoder module. We explored the data fusing techniques and stages for data fusing, the optimized end-to-end model architecture indicates an improved part quality prediction accuracy.
Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset
Venue: arXiv cs.CV 2024
Year: 2024

This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large dataset of gaze recordings from 9202 people with a level of eye tracking (ET) signal quality equivalent to modern consumer-facing virtual reality (VR) platforms. The size of the employed dataset is at least an order-of-magnitude larger than any other dataset from previous related work. Binocular estimates of the optical and visual axes of the eyes and a minimum duration for enrollment and verification are required for our model to achieve a false rejection rate (FRR) of below 3% at a false acceptance rate (FAR) of 1 in 50,000. In terms of identification accuracy which decreases with gallery size, we estimate that our model would fall below chance-level accuracy for gallery sizes of 148,000 or more. Our major findings indicate that gaze authentication can be as accurate as required by the FIDO standard when driven by a state-of-the-art machine learning architecture and a sufficiently large training dataset.
Holographic Parallax Improves 3D Perceptual Realism
Venue: arXiv cs.GR 2024
Year: 2024

Holographic near-eye displays are a promising technology to solve long-standing challenges in virtual and augmented reality display systems. Over the last few years, many different computer-generated holography (CGH) algorithms have been proposed that are supervised by different types of target content, such as 2.5D RGB-depth maps, 3D focal stacks, and 4D light fields. It is unclear, however, what the perceptual implications are of the choice of algorithm and target content type. In this work, we build a perceptual testbed of a full-color, high-quality holographic near-eye display. Under natural viewing conditions, we examine the effects of various CGH supervision formats and conduct user studies to assess their perceptual impacts on 3D realism. Our results indicate that CGH algorithms designed for specific viewpoints exhibit noticeable deficiencies in achieving 3D realism. In contrast, holograms incorporating parallax cues consistently outperform other formats across different viewing conditions, including the center of the eyebox. This finding is particularly interesting and suggests that the inclusion of parallax cues in CGH rendering plays a crucial role in enhancing the overall quality of the holographic experience. This work represents an initial stride towards delivering a perceptually realistic 3D experience with holographic near-eye displays.
Oracle-Augmented Prophet Inequalities
Venue: arXiv cs.GT 2024
Year: 2024

In the classical prophet inequality settings, a gambler is given a sequence of $n$ random variables $X_1, \dots, X_n$, taken from known distributions, observes their values in this (potentially adversarial) order, and select one of them, immediately after it is being observed, so that its value is as high as possible. The classical \emph{prophet inequality} shows a strategy that guarantees a value at least half of that an omniscience prophet that picks the maximum, and this ratio is optimal. Here, we generalize the prophet inequality, allowing the gambler some additional information about the future that is otherwise privy only to the prophet. Specifically, at any point in the process, the gambler is allowed to query an oracle $\mathcal{O}$. The oracle responds with a single bit answer: YES if the current realization is greater than the remaining realizations, and NO otherwise. We show that the oracle model with $m$ oracle calls is equivalent to the \textsc{Top-$1$-of-$(m+1)$} model when the objective is maximizing the probability of selecting the maximum. This equivalence fails to hold when the objective is maximizing the competitive ratio, but we still show that any algorithm for the oracle model implies an equivalent competitive ratio for the \textsc{Top-$1$-of-$(m+1)$} model. We resolve the oracle model for any $m$, giving tight lower and upper bound on the best possible competitive ratio compared to an almighty adversary. As a consequence, we provide new results as well as improvements on known results for the \textsc{Top-$1$-of-$m$} model.
AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height Large-scale Outdoor Scene Rendering
Venue: arXiv cs.CV 2024
Year: 2024

Existing neural radiance fields (NeRF)-based novel view synthesis methods for large-scale outdoor scenes are mainly built on a single altitude. Moreover, they often require a priori camera shooting height and scene scope, leading to inefficient and impractical applications when camera altitude changes. In this work, we propose an end-to-end framework, termed AG-NeRF, and seek to reduce the training cost of building good reconstructions by synthesizing free-viewpoint images based on varying altitudes of scenes. Specifically, to tackle the detail variation problem from low altitude (drone-level) to high altitude (satellite-level), a source image selection method and an attention-based feature fusion approach are developed to extract and fuse the most relevant features of target view from multi-height images for high-fidelity rendering. Extensive experiments demonstrate that AG-NeRF achieves SOTA performance on 56 Leonard and Transamerica benchmarks and only requires a half hour of training time to reach the competitive PSNR as compared to the latest BungeeNeRF.
Terrain-Aware Stride-Level Trajectory Forecasting for a Powered Hip Exoskeleton via Vision and Kinematics Fusion
Venue: arXiv cs.RO 2024
Year: 2024

Powered hip exoskeletons have shown the ability for locomotion assistance during treadmill walking. However, providing suitable assistance in real-world walking scenarios which involve changing terrain remains challenging. Recent research suggests that forecasting the lower limb joint's angles could provide target trajectories for exoskeletons and prostheses, and the performance could be improved with visual information. In this letter, We share a real-world dataset of 10 healthy subjects walking through five common types of terrain with stride-level label. We design a network called Sandwich Fusion Transformer for Image and Kinematics (SFTIK), which predicts the thigh angle of the ensuing stride given the terrain images at the beginning of the preceding and the ensuing stride and the IMU time series during the preceding stride. We introduce width-level patchify, tailored for egocentric terrain images, to reduce the computational demands. We demonstrate the proposed sandwich input and fusion mechanism could significantly improve the forecasting performance. Overall, the SFTIK outperforms baseline methods, achieving a computational efficiency of 3.31 G Flops, and root mean square error (RMSE) of 3.445 \textpm \ 0.804\textdegree \ and Pearson's correlation coefficient (PCC) of 0.971 \textpm\ 0.025. The results demonstrate that SFTIK could forecast the thigh's angle accurately with low computational cost, which could serve as a terrain adaptive trajectory planning method for hip exoskeletons. Codes and data are available at https://github.com/RuoqiZhao116/SFTIK.
Large Language Models: From Notes to Musical Form
Venue: arXiv cs.SD 2024
Year: 2024

While many topics of the learning-based approach to automated music generation are under active research, musical form is under-researched. In particular, recent methods based on deep learning models generate music that, at the largest time scale, lacks any structure. In practice, music longer than one minute generated by such models is either unpleasantly repetitive or directionless. Adapting a recent music generation model, this paper proposes a novel method to generate music with form. The experimental results show that the proposed method can generate 2.5-minute-long music that is considered as pleasant as the music used to train the model. The paper first reviews a recent music generation method based on language models (transformer architecture). We discuss why learning musical form by such models is infeasible. Then we discuss our proposed method and the experiments.