id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.19567 | Safe Interval Randomized Path Planning For Manipulators | [
"cs.RO"
] | Planning safe paths in 3D workspace for high DoF robotic systems, such as manipulators, is a challenging problem, especially when the environment is populated with the dynamic obstacles that need to be avoided. In this case the time dimension should be taken into account that further increases the complexity of plannin... | {
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2412.19571 | xFLIE: Leveraging Actionable Hierarchical Scene Representations for
Autonomous Semantic-Aware Inspection Missions | [
"cs.RO"
] | This article presents xFLIE, a fully integrated 3D hierarchical scene graph based autonomous inspection architecture. Specifically, we present a tightly-coupled solution of incremental 3D Layered Semantic Graphs (LSG) construction and real-time exploitation by a multi-modal autonomy, First-Look based Inspection and Exp... | {
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2412.19578 | Graph-attention-based Casual Discovery with Trust Region-navigated
Clipping Policy Optimization | [
"cs.LG",
"cs.AI"
] | In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle with unoriented edges or latent assumptions violation suffered by conventional methods, researchers formulated a reinforcement learning (RL) procedure for causal discovery, and equ... | {
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2412.19582 | An Actionable Hierarchical Scene Representation Enhancing Autonomous
Inspection Missions in Unknown Environments | [
"cs.RO"
] | In this article, we present the Layered Semantic Graphs (LSG), a novel actionable hierarchical scene graph, fully integrated with a multi-modal mission planner, the FLIE: A First-Look based Inspection and Exploration planner. The novelty of this work stems from aiming to address the task of maintaining an intuitive and... | {
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2412.19583 | A Comparative Study of Machine Unlearning Techniques for Image and Text
Classification Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | Machine Unlearning has emerged as a critical area in artificial intelligence, addressing the need to selectively remove learned data from machine learning models in response to data privacy regulations. This paper provides a comprehensive comparative analysis of six state-of-theart unlearning techniques applied to imag... | {
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2412.19584 | DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction | [
"cs.CV"
] | We propose a novel framework for scene decomposition and static background reconstruction from everyday videos. By integrating the trained motion masks and modeling the static scene as Gaussian splats with dynamics-aware optimization, our method achieves more accurate background reconstruction results than previous wor... | {
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2412.19585 | Ultralight Signal Classification Model for Automatic Modulation
Recognition | [
"cs.LG",
"eess.SP"
] | The growing complexity of radar signals demands responsive and accurate detection systems that can operate efficiently on resource-constrained edge devices. Existing models, while effective, often rely on substantial computational resources and large datasets, making them impractical for edge deployment. In this work, ... | {
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2412.19587 | Goal-oriented Communications based on Recursive Early Exit Neural
Networks | [
"cs.LG"
] | This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First, we introduce an innovative early exit strategy that dynamically partitions computations, enabling samples to be offloaded to a server based... | {
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2412.19589 | ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes
and Attention-based Feature Fusion | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | Drug-target interaction is fundamental in understanding how drugs affect biological systems, and accurately predicting drug-target affinity (DTA) is vital for drug discovery. Recently, deep learning methods have emerged as a significant approach for estimating the binding strength between drugs and target proteins. How... | {
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2412.19595 | SocRATES: Towards Automated Scenario-based Testing of Social Navigation
Algorithms | [
"cs.RO",
"cs.AI"
] | Current social navigation methods and benchmarks primarily focus on proxemics and task efficiency. While these factors are important, qualitative aspects such as perceptions of a robot's social competence are equally crucial for successful adoption and integration into human environments. We propose a more comprehensiv... | {
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2412.19601 | Arbitrarily Fast Multivariable Least-squares MRAC | [
"eess.SY",
"cs.SY"
] | A novel least-squares model-reference direct adaptive control (LS-MRAC) algorithm for multivariable (MIMO) plants is presented. The controller parameters are directly updated based on the output tracking error. The control law is crucially modified to reduce the relative degree of the error model to zero. A complete Ly... | {
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2412.19606 | Enhancing Fine-grained Image Classification through Attentive Batch
Training | [
"cs.CV"
] | Fine-grained image classification, which is a challenging task in computer vision, requires precise differentiation among visually similar object categories. In this paper, we propose 1) a novel module called Residual Relationship Attention (RRA) that leverages the relationships between images within each training batc... | {
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2412.19609 | Bidding Games on Markov Decision Processes with Quantitative
Reachability Objectives | [
"cs.GT",
"cs.AI"
] | Graph games are fundamental in strategic reasoning of multi-agent systems and their environments. We study a new family of graph games which combine stochastic environmental uncertainties and auction-based interactions among the agents, formalized as bidding games on (finite) Markov decision processes (MDP). Normally, ... | {
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2412.19610 | Machine Generated Product Advertisements: Benchmarking LLMs Against
Human Performance | [
"cs.CL"
] | This study compares the performance of AI-generated and human-written product descriptions using a multifaceted evaluation model. We analyze descriptions for 100 products generated by four AI models (Gemma 2B, LLAMA, GPT2, and ChatGPT 4) with and without sample descriptions, against human-written descriptions. Our eval... | {
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2412.19616 | Gradient Weight-normalized Low-rank Projection for Efficient LLM
Training | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) have shown remarkable performance across various tasks, but the escalating demands on computational resources pose significant challenges, particularly in the extensive utilization of full fine-tuning for downstream tasks. To address this, parameter-efficient fine-tuning (PEFT) methods have... | {
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2412.19628 | RecConv: Efficient Recursive Convolutions for Multi-Frequency
Representations | [
"cs.CV"
] | Recent advances in vision transformers (ViTs) have demonstrated the advantage of global modeling capabilities, prompting widespread integration of large-kernel convolutions for enlarging the effective receptive field (ERF). However, the quadratic scaling of parameter count and computational complexity (FLOPs) with resp... | {
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2412.19634 | Deep Linear Hawkes Processes | [
"stat.ML",
"cs.LG"
] | Marked temporal point processes (MTPPs) are used to model sequences of different types of events with irregular arrival times, with broad applications ranging from healthcare and social networks to finance. We address shortcomings in existing point process models by drawing connections between modern deep state-space m... | {
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2412.19637 | ReNeg: Learning Negative Embedding with Reward Guidance | [
"cs.CV"
] | In text-to-image (T2I) generation applications, negative embeddings have proven to be a simple yet effective approach for enhancing generation quality. Typically, these negative embeddings are derived from user-defined negative prompts, which, while being functional, are not necessarily optimal. In this paper, we intro... | {
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2412.19638 | Xmodel-2 Technical Report | [
"cs.AI"
] | Xmodel-2 is a 1.2-billion-parameter large language model designed specifically for reasoning tasks. Its architecture enables different model scales to share a unified set of hyperparameters, allowing for extensive experimentation on smaller models and seamless transfer of optimal configurations to larger models. To max... | {
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2412.19645 | VideoMaker: Zero-shot Customized Video Generation with the Inherent
Force of Video Diffusion Models | [
"cs.CV"
] | Zero-shot customized video generation has gained significant attention due to its substantial application potential. Existing methods rely on additional models to extract and inject reference subject features, assuming that the Video Diffusion Model (VDM) alone is insufficient for zero-shot customized video generation.... | {
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2412.19646 | Chimera: A Block-Based Neural Architecture Search Framework for
Event-Based Object Detection | [
"cs.CV",
"cs.AI"
] | Event-based cameras are sensors that simulate the human eye, offering advantages such as high-speed robustness and low power consumption. Established Deep Learning techniques have shown effectiveness in processing event data. Chimera is a Block-Based Neural Architecture Search (NAS) framework specifically designed for ... | {
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2412.19648 | Enhancing Vision-Language Tracking by Effectively Converting Textual
Cues into Visual Cues | [
"cs.CV",
"cs.MM"
] | Vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current datasets typically contain much more image data than text, limiting the ability of VLT methods to align the two modalities effectively. To ... | {
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2412.19650 | Toward Modality Gap: Vision Prototype Learning for Weakly-supervised
Semantic Segmentation with CLIP | [
"cs.CV",
"cs.LG"
] | The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilities. Existing methods attempt to optimize input text prompts for improved alignment of images and text, by finely adjusting text prototypes to... | {
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2412.19654 | Asymmetrical Reciprocity-based Federated Learning for Resolving
Disparities in Medical Diagnosis | [
"cs.LG",
"cs.DC"
] | Geographic health disparities pose a pressing global challenge, particularly in underserved regions of low- and middle-income nations. Addressing this issue requires a collaborative approach to enhance healthcare quality, leveraging support from medically more developed areas. Federated learning emerges as a promising ... | {
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2412.19662 | Innovation beyond intention: harnessing exaptation for technological
breakthroughs | [
"physics.soc-ph",
"cs.SI"
] | The frameworks that explore scientific and technological evolution suggest that discoveries and inventions are intrinsic processes, while the wealth of knowledge accumulated over time enables researchers to make further advancements, echoing Newton's sentiment of "standing on the shoulders of giants." Despite the expon... | {
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2412.19663 | CAD-GPT: Synthesising CAD Construction Sequence with Spatial
Reasoning-Enhanced Multimodal LLMs | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Computer-aided design (CAD) significantly enhances the efficiency, accuracy, and innovation of design processes by enabling precise 2D and 3D modeling, extensive analysis, and optimization. Existing methods for creating CAD models rely on latent vectors or point clouds, which are difficult to obtain and costly to store... | {
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2412.19669 | Toward Scalable Multirobot Control: Fast Policy Learning in Distributed
MPC | [
"cs.RO",
"cs.LG"
] | Distributed model predictive control (DMPC) is promising in achieving optimal cooperative control in multirobot systems (MRS). However, real-time DMPC implementation relies on numerical optimization tools to periodically calculate local control sequences online. This process is computationally demanding and lacks scala... | {
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2412.19675 | DLScanner: A parameter space scanner package assisted by deep learning
methods | [
"hep-ph",
"cs.CV",
"hep-ex",
"hep-th"
] | In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional scans and the limited generalization of the DL network when mapping random points... | {
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2412.19676 | Optimizing Local-Global Dependencies for Accurate 3D Human Pose
Estimation | [
"cs.CV"
] | Transformer-based methods have recently achieved significant success in 3D human pose estimation, owing to their strong ability to model long-range dependencies. However, relying solely on the global attention mechanism is insufficient for capturing the fine-grained local details, which are crucial for accurate pose es... | {
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2412.19677 | Deep ReLU networks -- injectivity capacity upper bounds | [
"stat.ML",
"cond-mat.dis-nn",
"cs.IT",
"cs.LG",
"math.IT"
] | We study deep ReLU feed forward neural networks (NN) and their injectivity abilities. The main focus is on \emph{precisely} determining the so-called injectivity capacity. For any given hidden layers architecture, it is defined as the minimal ratio between number of network's outputs and inputs which ensures unique rec... | {
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2412.19682 | A Hybrid Technique for Plant Disease Identification and Localisation in
Real-time | [
"cs.CV"
] | Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-ti... | {
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2412.19683 | Combining Machine Learning with Recurrence Analysis for resonance
detection | [
"gr-qc",
"cs.LG"
] | The width of a resonance in a nearly integrable system, i.e. in a non-integrable system where chaotic motion is still not prominent, can tell us how a perturbation parameter is driving the system away from integrability. Although the tool that we are presenting here can be used is quite generic and can be used in a var... | {
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2412.19684 | Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free,
Adaptive, Universal Prompt Optimization Framework | [
"cs.AI"
] | Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on resource-constrained devices. However, due to data privacy concerns, existing open-source EMLLMs rarely have access to private domain-specific... | {
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2412.19685 | A Large-scale Interpretable Multi-modality Benchmark for Facial Image
Forgery Localization | [
"cs.CV",
"cs.AI"
] | Image forgery localization, which centers on identifying tampered pixels within an image, has seen significant advancements. Traditional approaches often model this challenge as a variant of image segmentation, treating the binary segmentation of forged areas as the end product. We argue that the basic binary forgery m... | {
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2412.19688 | A Review on the Integration of Artificial Intelligence and Medical
Imaging in IVF Ovarian Stimulation | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Artificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows significant promise in supporting decision-making during the ovarian stimulation phase of the IVF process. This review evaluates studies focuse... | {
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2412.19696 | An Integrated Optimization and Deep Learning Pipeline for Predicting
Live Birth Success in IVF Using Feature Optimization and Transformer-Based
Models | [
"cs.AI"
] | In vitro fertilization (IVF) is a widely utilized assisted reproductive technology, yet predicting its success remains challenging due to the multifaceted interplay of clinical, demographic, and procedural factors. This study develops a robust artificial intelligence (AI) pipeline aimed at predicting live birth outcome... | {
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2412.19705 | Noise Sensitivity of the Semidefinite Programs for Direct Data-Driven
LQR | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this paper, we study the noise sensitivity of the semidefinite program (SDP) proposed for direct data-driven infinite-horizon linear quadratic regulator (LQR) problem for discrete-time linear time-invariant systems. While this SDP is shown to find the true LQR controller in the noise-free setting, we show that it le... | {
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2412.19706 | Geometric Freeze-Tag Problem | [
"cs.DC",
"cs.RO"
] | We study the Freeze-Tag Problem (FTP), introduced by Arkin et al. (SODA'02), where the objective is to activate a group of n robots, starting from a single initially active robot. Robots are positioned in $\mathbb{R}^d$, and once activated, they move at a constant speed to wake up others. The goal is to minimize the ti... | {
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2412.19707 | Toward Adaptive Reasoning in Large Language Models with Thought Rollback | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have been routinely used to solve various tasks using step-by-step reasoning. However, the structure of intermediate reasoning steps, or thoughts, is rigid and unidirectional, such as chains, trees, or acyclic-directed graphs. Consequently, the resulting inflexible and forward-only reasonin... | {
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2412.19711 | Causal machine learning for heterogeneous treatment effects in the
presence of missing outcome data | [
"stat.ML",
"cs.LG"
] | When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represented. In this work, we discuss this commonly overlooked problem and consider the impact that missing at random (MAR) outcome data has on causa... | {
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2412.19712 | From Elements to Design: A Layered Approach for Automatic Graphic Design
Composition | [
"cs.CV"
] | In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks and are far from achieving the design composition task; they d... | {
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2412.19713 | ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient
Liver Segmentation | [
"eess.IV",
"cs.CV",
"cs.LG"
] | The growing need for accurate and efficient 3D identification of tumors, particularly in liver segmentation, has spurred considerable research into deep learning models. While many existing architectures offer strong performance, they often face challenges such as overfitting and excessive computational costs. An adjus... | {
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2412.19718 | Text2Insight: Transform natural language text into insights seamlessly
using multi-model architecture | [
"cs.AI",
"cs.LG"
] | The growing demand for dynamic, user-centric data analysis and visualization is evident across domains like healthcare, finance, and research. Traditional visualization tools often fail to meet individual user needs due to their static and predefined nature. To address this gap, Text2Insight is introduced as an innovat... | {
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2412.19719 | Trading Off Energy Storage and Payload -- An Analytical Model for
Freight Train Configuration | [
"eess.SY",
"cs.SY",
"math.OC"
] | To support planning of alternative fuel technology (e.g., battery-electric locomotives) deployment for decarbonizing non-electrified freight rail, we develop a convex optimization formulation with a closed-form solution to determine the optimal number of energy storage tender cars in a train. The formulation shares a s... | {
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2412.19720 | Sharpening Neural Implicit Functions with Frequency Consolidation Priors | [
"cs.CV"
] | Signed Distance Functions (SDFs) are vital implicit representations to represent high fidelity 3D surfaces. Current methods mainly leverage a neural network to learn an SDF from various supervisions including signed distances, 3D point clouds, or multi-view images. However, due to various reasons including the bias of ... | {
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2412.19723 | OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse
Task Synthesis | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.HC"
] | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. Despite their utility in advancing digital automation, a critical bottleneck persists: collecting high-quality trajectory data for training. Common practices for collecting such data ... | {
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2412.19725 | EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs | [
"cs.LG"
] | Meta-learning, i.e., "learning to learn", is a promising approach to enable efficient BCI classifier training with limited amounts of data. It can effectively use collections of in some way similar classification tasks, with rapid adaptation to new tasks where only minimal data are available. However, applying meta-lea... | {
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2412.19726 | Position: Theory of Mind Benchmarks are Broken for Large Language Models | [
"cs.AI"
] | This position paper argues that the majority of theory of mind benchmarks are broken because of their inability to directly test how large language models (LLMs) adapt to new partners. This problem stems from the fact that theory of mind benchmarks for LLMs are overwhelmingly inspired by the methods used to test theory... | {
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2412.19727 | Learning to Forget: Bayesian Time Series Forecasting using Recurrent
Sparse Spectrum Signature Gaussian Processes | [
"stat.ML",
"cs.LG"
] | The signature kernel is a kernel between time series of arbitrary length and comes with strong theoretical guarantees from stochastic analysis. It has found applications in machine learning such as covariance functions for Gaussian processes. A strength of the underlying signature features is that they provide a struct... | {
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2412.19732 | Generative Pretrained Embedding and Hierarchical Irregular Time Series
Representation for Daily Living Activity Recognition | [
"cs.LG"
] | Within the evolving landscape of smart homes, the precise recognition of daily living activities using ambient sensor data stands paramount. This paper not only aims to bolster existing algorithms by evaluating two distinct pretrained embeddings suited for ambient sensor activations but also introduces a novel hierarch... | {
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2412.19737 | Adaptive Context-Aware Multi-Path Transmission Control for VR/AR
Content: A Deep Reinforcement Learning Approach | [
"cs.NI",
"cs.AI"
] | This paper introduces the Adaptive Context-Aware Multi-Path Transmission Control Protocol (ACMPTCP), an efficient approach designed to optimize the performance of Multi-Path Transmission Control Protocol (MPTCP) for data-intensive applications such as augmented and virtual reality (AR/VR) streaming. ACMPTCP addresses t... | {
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2412.19744 | AAM-SEALS: Developing Aerial-Aquatic Manipulators in SEa, Air, and Land
Simulator | [
"cs.RO"
] | Current mobile manipulators and high-fidelity simulators lack the ability to seamlessly operate and simulate across integrated environments spanning sea, air, and land. To address this gap, we introduce Aerial-Aquatic Manipulators (AAMs) in SEa, Air, and Land Simulator (SEALS), a comprehensive and photorealistic simula... | {
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2412.19747 | Enhancing Adversarial Robustness of Deep Neural Networks Through
Supervised Contrastive Learning | [
"cs.LG",
"cs.AI"
] | Adversarial attacks exploit the vulnerabilities of convolutional neural networks by introducing imperceptible perturbations that lead to misclassifications, exposing weaknesses in feature representations and decision boundaries. This paper presents a novel framework combining supervised contrastive learning and margin-... | {
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2412.19748 | UAV-Enabled Secure ISAC Against Dual Eavesdropping Threats: Joint
Beamforming and Trajectory Design | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this work, we study an unmanned aerial vehicle (UAV)-enabled secure integrated sensing and communication (ISAC) system, where a UAV serves as an aerial base station (BS) to simultaneously perform communication with a user and detect a target on the ground, while a dual-functional eavesdropper attempts to intercept t... | {
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2412.19750 | IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With
an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring
Distribution-Aware Data Reshaping | [
"cs.AR",
"cs.AI"
] | Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b convolutional neural networks (CNNs) at the edge. Yet, they commonly make use of a fixed dot-product (DP) voltage swing, which leads to a loss in effective ADC bits due to ... | {
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2412.19754 | Complement or substitute? How AI increases the demand for human skills | [
"econ.GN",
"cs.AI",
"q-fin.EC"
] | The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution ... | {
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2412.19755 | "Did my figure do justice to the answer?" : Towards Multimodal Short
Answer Grading with Feedback (MMSAF) | [
"cs.AI"
] | Assessments play a vital role in a student's learning process by providing feedback on a student's proficiency level in a subject. While assessments often make use of short answer questions, it is often difficult to grade such questions at a large scale. Moreover, such questions often involve students drawing supportin... | {
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2412.19759 | Enhancing Cognitive Diagnosis by Modeling Learner Cognitive Structure
State | [
"cs.AI"
] | Cognitive diagnosis represents a fundamental research area within intelligent education, with the objective of measuring the cognitive status of individuals. Theoretically, an individual's cognitive state is essentially equivalent to their cognitive structure state. Cognitive structure state comprises two key component... | {
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2412.19761 | Generative Video Propagation | [
"cs.CV"
] | Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative video propagation framework, various video tasks can be addressed in a unified way by leveraging the generative power of such models. Specifica... | {
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2412.19765 | From Ceilings to Walls: Universal Dynamic Perching of Small Aerial
Robots on Surfaces with Variable Orientations | [
"cs.RO",
"cs.LG"
] | This work demonstrates universal dynamic perching capabilities for quadrotors of various sizes and on surfaces with different orientations. By employing a non-dimensionalization framework and deep reinforcement learning, we systematically assessed how robot size and surface orientation affect landing capabilities. We h... | {
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2412.19770 | Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via
Multi-Turn Dialogue and Dual-Agent Integration | [
"cs.LG"
] | Translating legacy Fortran code into C++ is a crucial step in modernizing high-performance computing (HPC) applications. However, the scarcity of high-quality, parallel Fortran-to-C++ datasets and the limited domain-specific expertise in large language models (LLMs) present significant challenges for automated translat... | {
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2412.19774 | Analysis of Premature Death Rates in Texas Counties: The Impact of Air
Quality, Socioeconomic Factors, and COPD Prevalence | [
"cs.LG"
] | Understanding factors contributing to premature mortality is critical for public health planning. This study examines the relationships between premature death rates and multiple risk factors across several Texas counties, utilizing EPA air quality data, Census information, and county health records from recent years. ... | {
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2412.19775 | Improved image display by identifying the RGB family color space | [
"cs.CV"
] | To display an image, the color space in which the image is encoded is assumed to be known. Unfortunately, this assumption is rarely realistic. In this paper, we propose to identify the color space of a given color image using pixel embedding and the Gaussian process. Five color spaces are supported, namely Adobe RGB, A... | {
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2412.19778 | Symbolic Approximations to Ricci-flat Metrics Via Extrinsic Symmetries
of Calabi-Yau Hypersurfaces | [
"hep-th",
"cs.LG",
"math.AG",
"math.DG"
] | Ever since Yau's non-constructive existence proof of Ricci-flat metrics on Calabi-Yau manifolds, finding their explicit construction remains a major obstacle to development of both string theory and algebraic geometry. Recent computational approaches employ machine learning to create novel neural representations for ap... | {
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2412.19780 | Tensor Network Estimation of Distribution Algorithms | [
"cs.LG",
"quant-ph"
] | Tensor networks are a tool first employed in the context of many-body quantum physics that now have a wide range of uses across the computational sciences, from numerical methods to machine learning. Methods integrating tensor networks into evolutionary optimization algorithms have appeared in the recent literature. In... | {
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2412.19781 | Machine Learning for Sentiment Analysis of Imported Food in Trinidad and
Tobago | [
"cs.CL",
"cs.LG"
] | This research investigates the performance of various machine learning algorithms (CNN, LSTM, VADER, and RoBERTa) for sentiment analysis of Twitter data related to imported food items in Trinidad and Tobago. The study addresses three primary research questions: the comparative accuracy and efficiency of the algorithms,... | {
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2412.19784 | Can AI Help with Your Personal Finances? | [
"cs.AI",
"cs.CE",
"cs.LG",
"econ.GN",
"q-fin.EC"
] | In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilitie... | {
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2412.19785 | Enhancing Whisper's Accuracy and Speed for Indian Languages through
Prompt-Tuning and Tokenization | [
"cs.CL",
"eess.AS"
] | Automatic speech recognition has recently seen a significant advancement with large foundational models such as Whisper. However, these models often struggle to perform well in low-resource languages, such as Indian languages. This paper explores two novel approaches to enhance Whisper's multilingual speech recognition... | {
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2412.19792 | InfAlign: Inference-aware language model alignment | [
"cs.LG",
"cs.CL",
"cs.IT",
"math.IT"
] | Language model alignment is a critical step in training modern generative language models. Alignment targets to improve win rate of a sample from the aligned model against the base model. Today, we are increasingly using inference-time algorithms (e.g., Best-of-N, controlled decoding, tree search) to decode from langua... | {
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2412.19794 | MVTamperBench: Evaluating Robustness of Vision-Language Models | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have driven major advances in video understanding, yet their vulnerability to adversarial tampering and manipulations remains underexplored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tamperin... | {
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2412.19802 | LASER: A new method for locally adaptive nonparametric regression | [
"stat.ML",
"cs.LG",
"math.PR",
"math.ST",
"stat.ME",
"stat.TH"
] | In this article, we introduce \textsf{LASER} (Locally Adaptive Smoothing Estimator for Regression), a computationally efficient locally adaptive nonparametric regression method that performs variable bandwidth local polynomial regression. We prove that it adapts (near-)optimally to the local H\"{o}lder exponent of the ... | {
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2412.19804 | Universal MIMO Jammer Mitigation | [
"eess.SP",
"cs.IT",
"math.IT"
] | Multi-antenna processing enables jammer mitigation through spatial filtering, provided that the receiver knows the spatial signature of the jammer interference. Estimating this signature is easy for barrage jammers that transmit continuously and with static signature, but difficult for more sophisticated jammers. Smart... | {
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2412.19806 | Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating,
Segmenting, Editing | [
"cs.CV",
"cs.HC"
] | Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained instance-level understanding, lack of unified support for both images and videos, and insufficient coverage across various vision tasks. In this... | {
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2412.19808 | AI-driven Automation as a Pre-condition for Eudaimonia | [
"cs.CY",
"cs.AI"
] | The debate surrounding the 'future of work' is saturated with alarmist warnings about the loss of work as an intrinsically valuable activity. Instead, the present doctoral research approaches this debate from the perspective of human flourishing (eudaimonia). It articulates a neo-Aristotelian interpretation according t... | {
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2412.19811 | LINKs: Large Language Model Integrated Management for 6G Empowered
Digital Twin NetworKs | [
"cs.NI",
"cs.AI",
"cs.SY",
"eess.SY"
] | In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered DT networks, with a focus on optimizing data retrieval and communication efficien... | {
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} |
2412.19812 | Pharmacophore-guided de novo drug design with diffusion bridge | [
"q-bio.BM",
"cs.LG"
] | De novo design of bioactive drug molecules with potential to treat desired biological targets is a profound task in the drug discovery process. Existing approaches tend to leverage the pocket structure of the target protein to condition the molecule generation. However, even the pocket area of the target protein may co... | {
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} |
2412.19813 | Coverage Path Planning in Precision Agriculture: Algorithms,
Applications, and Key Benefits | [
"cs.RO"
] | Coverage path planning (CPP) is the task of computing an optimal path within a region to completely scan or survey an area of interest using one or multiple mobile robots. Robots equipped with sensors and cameras can collect vast amounts of data on crop health, soil conditions, and weather patterns. Advanced analytics ... | {
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2412.19814 | Predicting Human Brain States with Transformer | [
"q-bio.NC",
"cs.AI",
"cs.LG"
] | The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance imaging (fMRI), we can observe blood oxygen level-dependent (BOLD) changes, reflecting neural activity, to infer brain states and dynamics.... | {
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2412.19815 | Enhancing Drug-Target Interaction Prediction through Transfer Learning
from Activity Cliff Prediction Tasks | [
"q-bio.BM",
"cs.LG"
] | Recently, machine learning (ML) has gained popularity in the early stages of drug discovery. This trend is unsurprising given the increasing volume of relevant experimental data and the continuous improvement of ML algorithms. However, conventional models, which rely on the principle of molecular similarity, often fail... | {
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2412.19819 | ChipAlign: Instruction Alignment in Large Language Models for Chip
Design via Geodesic Interpolation | [
"cs.AR",
"cs.AI"
] | Recent advancements in large language models (LLMs) have expanded their application across various domains, including chip design, where domain-adapted chip models like ChipNeMo have emerged. However, these models often struggle with instruction alignment, a crucial capability for LLMs that involves following explicit ... | {
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2412.19820 | GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head
Projection | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent low-rank training methods, such as GaLore, have significantly reduced the memory required to optimize large language models (LLMs). However, these methods often suffer from time-consuming low-rank projection estimations. In particular, the singular value decomposition (SVD) in GaLore can consume more than 80\% o... | {
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} |
2412.19821 | Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive
Microexponents, and Code Recycling for Direct-Cast Compression of Large
Language Models | [
"cs.AR",
"cs.AI",
"cs.DC",
"cs.LG"
] | As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity challenges. Recently, AMD, Arm, Intel, Meta, Microsoft, NVIDIA, and Qualcomm have proposed a Microscaling standard (Mx), which augments block f... | {
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} |
2412.19823 | A Survey on Large Language Models for Communication, Network, and
Service Management: Application Insights, Challenges, and Future Directions | [
"cs.NI",
"cs.AI",
"cs.LG"
] | The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous... | {
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} |
2412.19824 | AnalogXpert: Automating Analog Topology Synthesis by Incorporating
Circuit Design Expertise into Large Language Models | [
"cs.AR",
"cs.AI",
"cs.SE"
] | Analog circuits are crucial in modern electronic systems, and automating their design has attracted significant research interest. One of major challenges is topology synthesis, which determines circuit components and their connections. Recent studies explore large language models (LLM) for topology synthesis. However,... | {
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2412.19825 | From Raw Data to Structural Semantics: Trade-offs among Distortion,
Rate, and Inference Accuracy | [
"cs.IT",
"math.IT"
] | This work explores the advantages of using persistence diagrams (PDs), topological signatures of raw point cloud data, in a point-to-point communication setting. PD is a structural semantics in the sense that it carries information about the shape and structure of the data. Instead of transmitting raw data, the transmi... | {
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2412.19828 | Quantum Implicit Neural Compression | [
"eess.IV",
"cs.CV",
"cs.LG",
"math.QA"
] | Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstruction for relatively low-resolution signals, the accuracy of high-frequency details is significantly de... | {
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2412.19829 | GFormer: Accelerating Large Language Models with Optimized Transformers
on Gaudi Processors | [
"cs.AR",
"cs.LG"
] | Heterogeneous hardware like Gaudi processor has been developed to enhance computations, especially matrix operations for Transformer-based large language models (LLMs) for generative AI tasks. However, our analysis indicates that Transformers are not fully optimized on such emerging hardware, primarily due to inadequat... | {
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} |
2412.19830 | A Unified Framework for Context-Aware IoT Management and
State-of-the-Art IoT Traffic Anomaly Detection | [
"cs.NI",
"cs.AI"
] | The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection... | {
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} |
2412.19832 | Back To The Future: A Hybrid Transformer-XGBoost Model for
Action-oriented Future-proofing Nowcasting | [
"cs.LG",
"cs.AI"
] | Inspired by the iconic movie Back to the Future, this paper explores an innovative adaptive nowcasting approach that reimagines the relationship between present actions and future outcomes. In the movie, characters travel through time to manipulate past events, aiming to create a better future. Analogously, our framewo... | {
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2412.19833 | Multi-atlas Ensemble Graph Neural Network Model For Major Depressive
Disorder Detection Using Functional MRI Data | [
"cs.CV",
"cs.AI"
] | Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies o... | {
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} |
2412.19834 | RoboSignature: Robust Signature and Watermarking on Network Attacks | [
"cs.CR",
"cs.AI",
"cs.CV",
"cs.LG"
] | Generative models have enabled easy creation and generation of images of all kinds given a single prompt. However, this has also raised ethical concerns about what is an actual piece of content created by humans or cameras compared to model-generated content like images or videos. Watermarking data generated by modern ... | {
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} |
2412.19835 | Multi-Agent Q-Learning for Real-Time Load Balancing User Association and
Handover in Mobile Networks | [
"eess.SP",
"cs.LG",
"cs.MA",
"cs.NI"
] | As next generation cellular networks become denser, associating users with the optimal base stations at each time while ensuring no base station is overloaded becomes critical for achieving stable and high network performance. We propose multi-agent online Q-learning (QL) algorithms for performing real-time load balanc... | {
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} |
2412.19836 | Reduced Order Models and Conditional Expectation -- Analysing Parametric
Low-Order Approximations | [
"cs.LG",
"cs.NA",
"math.NA"
] | Systems may depend on parameters which one may control, or which serve to optimise the system, or are imposed externally, or they could be uncertain. This last case is taken as the ``Leitmotiv'' for the following. A reduced order model is produced from the full order model by some kind of projection onto a relatively l... | {
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} |
2412.19837 | Data Poisoning Attacks to Local Differential Privacy Protocols for
Graphs | [
"cs.CR",
"cs.DB"
] | Graph analysis has become increasingly popular with the prevalence of big data and machine learning. Traditional graph data analysis methods often assume the existence of a trusted third party to collect and store the graph data, which does not align with real-world situations. To address this, some research has propos... | {
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} |
2412.19839 | Multi-View Fusion Neural Network for Traffic Demand Prediction | [
"cs.CV"
] | The extraction of spatial-temporal features is a crucial research in transportation studies, and current studies typically use a unified temporal modeling mechanism and fixed spatial graph for this purpose. However, the fixed spatial graph restricts the extraction of spatial features for similar but not directly connec... | {
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} |
2412.19840 | ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent
Document Processing | [
"cs.CV",
"cs.HC",
"cs.IR"
] | This paper presents ERPA, an innovative Robotic Process Automation (RPA) model designed to enhance ID data extraction and optimize Optical Character Recognition (OCR) tasks within immigration workflows. Traditional RPA solutions often face performance limitations when processing large volumes of documents, leading to i... | {
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} |
2412.19841 | FlameGS: Reconstruct flame light field via Gaussian Splatting | [
"cs.CV",
"eess.IV"
] | To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, ou... | {
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} |
2412.19842 | Multimodal joint prediction of traffic spatial-temporal data with graph
sparse attention mechanism and bidirectional temporal convolutional network | [
"cs.CV"
] | Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, exist... | {
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} |
2412.19844 | A Review of Latent Representation Models in Neuroimaging | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Neuroimaging data, particularly from techniques like MRI or PET, offer rich but complex information about brain structure and activity. To manage this complexity, latent representation models - such as Autoencoders, Generative Adversarial Networks (GANs), and Latent Diffusion Models (LDMs) - are increasingly applied. T... | {
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} |
2412.19845 | Unveiling Secrets of Brain Function With Generative Modeling: Motion
Perception in Primates & Cortical Network Organization in Mice | [
"q-bio.NC",
"cs.AI"
] | This Dissertation is comprised of two main projects, addressing questions in neuroscience through applications of generative modeling. Project #1 (Chapter 4) explores how neurons encode features of the external world. I combine Helmholtz's "Perception as Unconscious Inference" -- paralleled by modern generative model... | {
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} |
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