id stringlengths 9 16 | title stringlengths 4 278 | abstract stringlengths 3 4.08k | cs.HC bool 2 classes | cs.CE bool 2 classes | cs.SD bool 2 classes | cs.SI bool 2 classes | cs.AI bool 2 classes | cs.IR bool 2 classes | cs.LG bool 2 classes | cs.RO bool 2 classes | cs.CL bool 2 classes | cs.IT bool 2 classes | cs.SY bool 2 classes | cs.CV bool 2 classes | cs.CR bool 2 classes | cs.CY bool 2 classes | cs.MA bool 2 classes | cs.NE bool 2 classes | cs.DB bool 2 classes | Other bool 2 classes | __index_level_0__ int64 0 541k |
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2409.00359 | Predicting Femicide in Veracruz: A Fuzzy Logic Approach with the
Expanded MFM-FEM-VER-CP-2024 Model | The article focuses on the urgent issue of femicide in Veracruz, Mexico, and the development of the MFM_FEM_VER_CP_2024 model, a mathematical framework designed to predict femicide risk using fuzzy logic. This model addresses the complexity and uncertainty inherent in gender based violence by formalizing risk factors such as coercive control, dehumanization, and the cycle of violence. These factors are mathematically modeled through membership functions that assess the degree of risk associated with various conditions, including personal relationships and specific acts of violence. The study enhances the original model by incorporating new rules and refining existing membership functions, which significantly improve the model predictive accuracy. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 484,902 |
2412.03575 | Leveraging Large Language Models for Generating Labeled Mineral Site
Record Linkage Data | Record linkage integrates diverse data sources by identifying records that refer to the same entity. In the context of mineral site records, accurate record linkage is crucial for identifying and mapping mineral deposits. Properly linking records that refer to the same mineral deposit helps define the spatial coverage of mineral areas, benefiting resource identification and site data archiving. Mineral site record linkage falls under the spatial record linkage category since the records contain information about the physical locations and non-spatial attributes in a tabular format. The task is particularly challenging due to the heterogeneity and vast scale of the data. While prior research employs pre-trained discriminative language models (PLMs) on spatial entity linkage, they often require substantial amounts of curated ground-truth data for fine-tuning. Gathering and creating ground truth data is both time-consuming and costly. Therefore, such approaches are not always feasible in real-world scenarios where gold-standard data are unavailable. Although large generative language models (LLMs) have shown promising results in various natural language processing tasks, including record linkage, their high inference time and resource demand present challenges. We propose a method that leverages an LLM to generate training data and fine-tune a PLM to address the training data gap while preserving the efficiency of PLMs. Our approach achieves over 45\% improvement in F1 score for record linkage compared to traditional PLM-based methods using ground truth data while reducing the inference time by nearly 18 times compared to relying on LLMs. Additionally, we offer an automated pipeline that eliminates the need for human intervention, highlighting this approach's potential to overcome record linkage challenges. | false | false | false | false | true | true | false | false | true | false | false | false | false | false | false | false | false | false | 514,014 |
1910.04041 | Hierarchical Deep Double Q-Routing | This paper explores a deep reinforcement learning approach applied to the packet routing problem with high-dimensional constraints instigated by dynamic and autonomous communication networks. Our approach is motivated by the fact that centralized path calculation approaches are often not scalable, whereas the distributed approaches with locally acting nodes are not fully aware of the end-to-end performance. We instead hierarchically distribute the path calculation over designated nodes in the network while taking into account the end-to-end performance. Specifically, we develop a hierarchical cluster-oriented adaptive per-flow path calculation mechanism by leveraging the Deep Double Q-network (DDQN) algorithm, where the end-to-end paths are calculated by the source nodes with the assistance of cluster (group) leaders at different hierarchical levels. In our approach, a deferred composite reward is designed to capture the end-to-end performance through a feedback signal from the source nodes to the group leaders and captures the local network performance through the local resource assessments by the group leaders. This approach scales in large networks, adapts to the dynamic demand, utilizes the network resources efficiently and can be applied to segment routing. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | true | false | false | true | 148,660 |
1908.05877 | Zero-Shot Crowd Behavior Recognition | Understanding crowd behavior in video is challenging for computer vision. There have been increasing attempts on modeling crowded scenes by introducing ever larger property ontologies (attributes) and annotating ever larger training datasets. However, in contrast to still images, manually annotating video attributes needs to consider spatiotemporal evolution which is inherently much harder and more costly. Critically, the most interesting crowd behaviors captured in surveillance videos (e.g., street fighting, flash mobs) are either rare, thus have few examples for model training, or unseen previously. Existing crowd analysis techniques are not readily scalable to recognize novel (unseen) crowd behaviors. To address this problem, we investigate and develop methods for recognizing visual crowd behavioral attributes without any training samples, i.e., zero-shot learning crowd behavior recognition. To that end, we relax the common assumption that each individual crowd video instance is only associated with a single crowd attribute. Instead, our model learns to jointly recognize multiple crowd behavioral attributes in each video instance by exploring multiattribute cooccurrence as contextual knowledge for optimizing individual crowd attribute recognition. Joint multilabel attribute prediction in zero-shot learning is inherently nontrivial because cooccurrence statistics does not exist for unseen attributes. To solve this problem, we learn to predict cross-attribute cooccurrence from both online text corpus and multilabel annotation of videos with known attributes. Our experiments show that this approach to modeling multiattribute context not only improves zero-shot crowd behavior recognition on the WWW crowd video dataset, but also generalizes to novel behavior (violence) detection cross-domain in the Violence Flow video dataset. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 141,847 |
2203.01640 | Risk-aware Stochastic Shortest Path | We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models. | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | 283,459 |
2305.15071 | Towards Cyber Security for Low-Carbon Transportation: Overview,
Challenges and Future Directions | In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned. | false | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | 367,435 |
2012.00501 | A Statistical Real-Time Prediction Model for Recommender System | Recommender system has become an inseparable part of online shopping and its usability is increasing with the advancement of these e-commerce sites. An effective and efficient recommender system benefits both the seller and the buyer significantly. We considered user activities and product information for the filtering process in our proposed recommender system. Our model has achieved inspiring result (approximately 58% true-positive and 13% false-positive) for the data set provided by RecSys Challenge 2015. This paper aims to describe a statistical model that will help to predict the buying behavior of a user in real-time during a session. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 209,161 |
1702.04333 | On the Relevance of Auditory-Based Gabor Features for Deep Learning in
Automatic Speech Recognition | Previous studies support the idea of merging auditory-based Gabor features with deep learning architectures to achieve robust automatic speech recognition, however, the cause behind the gain of such combination is still unknown. We believe these representations provide the deep learning decoder with more discriminable cues. Our aim with this paper is to validate this hypothesis by performing experiments with three different recognition tasks (Aurora 4, CHiME 2 and CHiME 3) and assess the discriminability of the information encoded by Gabor filterbank features. Additionally, to identify the contribution of low, medium and high temporal modulation frequencies subsets of the Gabor filterbank were used as features (dubbed LTM, MTM and HTM respectively). With temporal modulation frequencies between 16 and 25 Hz, HTM consistently outperformed the remaining ones in every condition, highlighting the robustness of these representations against channel distortions, low signal-to-noise ratios and acoustically challenging real-life scenarios with relative improvements from 11 to 56% against a Mel-filterbank-DNN baseline. To explain the results, a measure of similarity between phoneme classes from DNN activations is proposed and linked to their acoustic properties. We find this measure to be consistent with the observed error rates and highlight specific differences on phoneme level to pinpoint the benefit of the proposed features. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 68,246 |
2102.04074 | Learning Curve Theory | Recently a number of empirical "universal" scaling law papers have been published, most notably by OpenAI. `Scaling laws' refers to power-law decreases of training or test error w.r.t. more data, larger neural networks, and/or more compute. In this work we focus on scaling w.r.t. data size $n$. Theoretical understanding of this phenomenon is largely lacking, except in finite-dimensional models for which error typically decreases with $n^{-1/2}$ or $n^{-1}$, where $n$ is the sample size. We develop and theoretically analyse the simplest possible (toy) model that can exhibit $n^{-\beta}$ learning curves for arbitrary power $\beta>0$, and determine whether power laws are universal or depend on the data distribution. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 218,985 |
2203.01325 | Self-Supervised Learning for Real-World Super-Resolution from Dual
Zoomed Observations | In this paper, we consider two challenging issues in reference-based super-resolution (RefSR), (i) how to choose a proper reference image, and (ii) how to learn real-world RefSR in a self-supervised manner. Particularly, we present a novel self-supervised learning approach for real-world image SR from observations at dual camera zooms (SelfDZSR). Considering the popularity of multiple cameras in modern smartphones, the more zoomed (telephoto) image can be naturally leveraged as the reference to guide the SR of the lesser zoomed (short-focus) image. Furthermore, SelfDZSR learns a deep network to obtain the SR result of short-focus image to have the same resolution as the telephoto image. For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch. To mitigate the effect of the misalignment between short-focus low-resolution (LR) image and telephoto ground-truth (GT) image, we design an auxiliary-LR generator and map the GT to an auxiliary-LR while keeping the spatial position unchanged. Then the auxiliary-LR can be utilized to deform the LR features by the proposed adaptive spatial transformer networks (AdaSTN), and match the Ref features to GT. During testing, SelfDZSR can be directly deployed to super-solve the whole short-focus image with the reference of telephoto image. Experiments show that our method achieves better quantitative and qualitative performance against state-of-the-arts. Codes are available at https://github.com/cszhilu1998/SelfDZSR. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 283,341 |
0901.3467 | Erasure Codes with a Banded Structure for Hybrid Iterative-ML Decoding | This paper presents new FEC codes for the erasure channel, LDPC-Band, that have been designed so as to optimize a hybrid iterative-Maximum Likelihood (ML) decoding. Indeed, these codes feature simultaneously a sparse parity check matrix, which allows an efficient use of iterative LDPC decoding, and a generator matrix with a band structure, which allows fast ML decoding on the erasure channel. The combination of these two decoding algorithms leads to erasure codes achieving a very good trade-off between complexity and erasure correction capability. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 3,029 |
1802.01039 | Stochastic simulation of pattern formation in growing tissue: a
multilevel approach | We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie's celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single cell computational modeling, here taken to be spatial stochastic reaction-diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch-Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites as well as via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 89,527 |
1909.04134 | Option Encoder: A Framework for Discovering a Policy Basis in
Reinforcement Learning | Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent. However, such techniques often yield a large number of options or skills, which can potentially be represented succinctly by filtering out any redundant information. Such a reduction can reduce the required computation while also improving the performance on a target task. In order to compress an array of option policies, we attempt to find a policy basis that accurately captures the set of all options. In this work, we propose Option Encoder, an auto-encoder based framework with intelligently constrained weights, that helps discover a collection of basis policies. The policy basis can be used as a proxy for the original set of skills in a suitable hierarchically organized framework. We demonstrate the efficacy of our method on a collection of grid-worlds and on the high-dimensional Fetch-Reach robotic manipulation task by evaluating the obtained policy basis on a set of downstream tasks. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 144,703 |
2306.13693 | RSMA for Overloaded MIMO Networks: Low-Complexity Design for Max-Min
Fairness | Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for multi-antenna wireless networks. In this work, we study the performance of RSMA in downlink overloaded networks, where the number of transmit antennas is smaller than the number of users. We provide analysis and closed-form solutions for optimal power and rate allocations that maximize max-min fairness when low-complexity precoding schemes are employed. The derived closed-form solutions are used to propose a low-complexity RSMA system design for precoder selection and resource allocation for arbitrary number of users and antennas under perfect and imperfect Channel State Information at the Transmitter (CSIT). We compare the performance of the proposed design with benchmark designs based on Space Division Multiple Access (SDMA) with and without user scheduling. By numerical results, we show that the proposed low-complexity RSMA design achieves a significantly higher rate compared to the SDMA-based benchmark designs under perfect and imperfect CSIT. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 375,365 |
1704.01286 | Dynamic Conjunctive Queries | The article investigates classes of queries maintainable by conjunctive queries (CQs) and their extensions and restrictions in the dynamic complexity framework of Patnaik and Immerman. Starting from the basic language of quantifier-free conjunctions of positive atoms, it studies the impact of additional operators and features - such as union, atomic negation and quantification - on the dynamic expressiveness, for the standard semantics as well as for Delta-semantics. Although many different combinations of these features are possible, they basically yield five important fragments for the standard semantics, characterized by the addition of (1) arbitrary quantification and atomic negation, (2) existential quantification and atomic negation, (3) existential quantification, (4) atomic negation (but no quantification), and by (5) no addition to the basic language at all. While fragments (3), (4) and (5) can be separated, it remains open whether fragments (1), (2) and (3) are actually different. The fragments arising from Delta-semantics are also subsumed by the standard fragments (1), (2) and (4). The main fragments of DynFO that had been studied in previous work, DynQF and DynProp, characterized by quantifier-free update programs with or without auxiliary functions, respectively, also fit into this hierarchy: DynProp coincides with fragment (4) and DynQF is strictly above fragment (4) and within fragment (3). As a further result, all (statically) FO-definable queries are captured by fragment (2) and a complete characterization of these queries in terms of non-recursive dynamic programs with existential update formulas with one existential quantifier is given. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | true | 71,235 |
1907.01197 | Treant: Training Evasion-Aware Decision Trees | Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force prediction errors. In this paper we focus on evasion attacks against decision tree ensembles, which are among the most successful predictive models for dealing with non-perceptual problems. Even though they are powerful and interpretable, decision tree ensembles have received only limited attention by the security and machine learning communities so far, leading to a sub-optimal state of the art for adversarial learning techniques. We thus propose Treant, a novel decision tree learning algorithm that, on the basis of a formal threat model, minimizes an evasion-aware loss function at each step of the tree construction. Treant is based on two key technical ingredients: robust splitting and attack invariance, which jointly guarantee the soundness of the learning process. Experimental results on three publicly available datasets show that Treant is able to generate decision tree ensembles that are at the same time accurate and nearly insensitive to evasion attacks, outperforming state-of-the-art adversarial learning techniques. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 137,260 |
2412.18771 | RIS-Assisted MIMO CV-QKD at THz Frequencies: Channel Estimation and SKR
Analysis | In this paper, a multiple-input multiple-output (MIMO) wireless system incorporating a reconfigurable intelligent surface (RIS) to efficiently operate at terahertz (THz) frequencies is considered. The transmitter, Alice, employs continuous-variable quantum key distribution (CV-QKD) to communicate secret keys to the receiver, Bob, which utilizes either homodyne or heterodyne detection. The latter node applies the least-squared approach to estimate the effective MIMO channel gain matrix prior to receiving the secret key, and this estimation is made available to Alice via an error-free feedback channel. An eavesdropper, Eve, is assumed to employ a collective Gaussian entanglement attack on the feedback channel to avail the estimated channel state information. We present a novel closed-form expression for the secret key rate (SKR) performance of the proposed RIS-assisted THz CV-QKD system. The effect of various system parameters, such as the number of RIS elements and their phase configurations, the channel estimation error, and the detector noise, on the SKR performance are studied via numerical evaluation of the derived formula. It is demonstrated that the RIS contributes to larger SKR for larger link distances, and that heterodyne detection is preferable over homodyne at lower pilot symbol powers. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 520,562 |
1906.02126 | Extractive Summarization via Weighted Dissimilarity and Importance
Aligned Key Iterative Algorithm | We present importance aligned key iterative algorithm for extractive summarization that is faster than conventional algorithms keeping its accuracy. The computational complexity of our algorithm is O($SNlogN$) to summarize original $N$ sentences into final $S$ sentences. Our algorithm maximizes the weighted dissimilarity defined by the product of importance and cosine dissimilarity so that the summary represents the document and at the same time the sentences of the summary are not similar to each other. The weighted dissimilarity is heuristically maximized by iterative greedy search and binary search to the sentences ordered by importance. We finally show a benchmark score based on summarization of customer reviews of products, which highlights the quality of our algorithm comparable to human and existing algorithms. We provide the source code of our algorithm on github https://github.com/qhapaq-49/imakita . | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 133,950 |
1802.06159 | Ad Hoc Table Retrieval using Semantic Similarity | We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 90,602 |
2108.12811 | Airplane Type Identification Based on Mask RCNN and Drone Images | For dealing with traffic bottlenecks at airports, aircraft object detection is insufficient. Every airport generally has a variety of planes with various physical and technological requirements as well as diverse service requirements. Detecting the presence of new planes will not address all traffic congestion issues. Identifying the type of airplane, on the other hand, will entirely fix the problem because it will offer important information about the plane's technical specifications (i.e., the time it needs to be served and its appropriate place in the airport). Several studies have provided various contributions to address airport traffic jams; however, their ultimate goal was to determine the existence of airplane objects. This paper provides a practical approach to identify the type of airplane in airports depending on the results provided by the airplane detection process using mask region convolution neural network. The key feature employed to identify the type of airplane is the surface area calculated based on the results of airplane detection. The surface area is used to assess the estimated cabin length which is considered as an additional key feature for identifying the airplane type. The length of any detected plane may be calculated by measuring the distance between the detected plane's two furthest points. The suggested approach's performance is assessed using average accuracies and a confusion matrix. The findings show that this method is dependable. This method will greatly aid in the management of airport traffic congestion. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 252,615 |
1308.2772 | Extended Distributed Learning Automata:A New Method for Solving
Stochastic Graph Optimization Problems | In this paper, a new structure of cooperative learning automata so-called extended learning automata (eDLA) is introduced. Based on the proposed structure, a new iterative randomized heuristic algorithm for finding optimal sub-graph in a stochastic edge-weighted graph through sampling is proposed. It has been shown that the proposed algorithm based on new networked-structure can be to solve the optimization problems on stochastic graph through less number of sampling in compare to standard sampling. Stochastic graphs are graphs in which the edges have an unknown distribution probability weights. Proposed algorithm uses an eDLA to find a policy that leads to an induced sub-graph that satisfies some restrictions such as minimum or maximum weight (length). At each stage of the proposed algorithm, eDLA determines which edges to be sampled. This eDLA-based proposed sampling method may result in decreasing unnecessary samples and hence decreasing the time that algorithm requires for finding the optimal sub-graph. It has been shown that proposed method converge to optimal solution, furthermore the probability of this convergence can be made arbitrarily close to 1 by using a sufficiently small learning rate. A new variance-aware threshold value was proposed that can be improving significantly convergence rate of the proposed eDLA-based algorithm. It has been shown that the proposed algorithm is competitive in terms of the quality of the solution | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 26,406 |
1805.08525 | Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing | Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influenced propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage. | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 98,161 |
2112.00712 | STEM: Unsupervised STructural EMbedding for Stance Detection | Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion - we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework. | false | false | false | true | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 269,220 |
2107.02955 | Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning | Legged robots need to be capable of walking on diverse terrain conditions. In this paper, we present a novel reinforcement learning framework for learning locomotion on non-rigid dynamic terrains. Specifically, our framework can generate quadruped locomotion on flat elastic terrain that consists of a matrix of tiles moving up and down passively when pushed by the robot's feet. A trained robot with 55cm base length can walk on terrain that can sink up to 5cm. We propose a set of observation and reward terms that enable this locomotion; in which we found that it is crucial to include the end-effector history and end-effector velocity terms into observation. We show the effectiveness of our method by training the robot with various terrain conditions. | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | 244,991 |
2210.08184 | Label distribution learning via label correlation grid | Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artificial or environmental factors. To alleviate this problem, we propose a \textbf{L}abel \textbf{C}orrelation \textbf{G}rid (LCG) to model the uncertainty of label relationships. Specifically, we compute a covariance matrix for the label space in the training set to represent the relationships between labels, then model the information distribution (Gaussian distribution function) for each element in the covariance matrix to obtain an LCG. Finally, our network learns the LCG to accurately estimate the label distribution for each instance. In addition, we propose a label distribution projection algorithm as a regularization term in the model training process. Extensive experiments verify the effectiveness of our method on several real benchmarks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 324,028 |
2502.07480 | Overfitting Regimes of Nadaraya-Watson Interpolators | In recent years, there has been much interest in understanding the generalization behavior of interpolating predictors, which overfit on noisy training data. Whereas standard analyses are concerned with whether a method is consistent or not, recent observations have shown that even inconsistent predictors can generalize well. In this work, we revisit the classic interpolating Nadaraya-Watson (NW) estimator (also known as Shepard's method), and study its generalization capabilities through this modern viewpoint. In particular, by varying a single bandwidth-like hyperparameter, we prove the existence of multiple overfitting behaviors, ranging non-monotonically from catastrophic, through benign, to tempered. Our results highlight how even classical interpolating methods can exhibit intricate generalization behaviors. Numerical experiments complement our theory, demonstrating the same phenomena. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 532,624 |
1912.07405 | RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk
Kick, Push Recovery, and Team Play Capabilities | Individual and team capabilities are challenged every year by rule changes and the increasing performance of the soccer teams at RoboCup Humanoid League. For RoboCup 2019 in the AdultSize class, the number of players (2 vs. 2 games) and the field dimensions were increased, which demanded for team coordination and robust visual perception and localization modules. In this paper, we present the latest developments that lead team NimbRo to win the soccer tournament, drop-in games, technical challenges and the Best Humanoid Award of the RoboCup Humanoid League 2019 in Sydney. These developments include a deep learning vision system, in-walk kicks, step-based push-recovery, and team play strategies. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 157,600 |
1805.09733 | Towards Robust Evaluations of Continual Learning | Experiments used in current continual learning research do not faithfully assess fundamental challenges of learning continually. Instead of assessing performance on challenging and representative experiment designs, recent research has focused on increased dataset difficulty, while still using flawed experiment set-ups. We examine standard evaluations and show why these evaluations make some continual learning approaches look better than they are. We introduce desiderata for continual learning evaluations and explain why their absence creates misleading comparisons. Based on our desiderata we then propose new experiment designs which we demonstrate with various continual learning approaches and datasets. Our analysis calls for a reprioritization of research effort by the community. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 98,485 |
2011.09080 | Deep Positional and Relational Feature Learning for Rotation-Invariant
Point Cloud Analysis | In this paper we propose a rotation-invariant deep network for point clouds analysis. Point-based deep networks are commonly designed to recognize roughly aligned 3D shapes based on point coordinates, but suffer from performance drops with shape rotations. Some geometric features, e.g., distances and angles of points as inputs of network, are rotation-invariant but lose positional information of points. In this work, we propose a novel deep network for point clouds by incorporating positional information of points as inputs while yielding rotation-invariance. The network is hierarchical and relies on two modules: a positional feature embedding block and a relational feature embedding block. Both modules and the whole network are proven to be rotation-invariant when processing point clouds as input. Experiments show state-of-the-art classification and segmentation performances on benchmark datasets, and ablation studies demonstrate effectiveness of the network design. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 207,087 |
2411.06823 | Large Language Model in Medical Informatics: Direct Classification and
Enhanced Text Representations for Automatic ICD Coding | Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper explores the use of Large Language Models (LLM), specifically the LLAMA architecture, to enhance ICD code classification through two methodologies: direct application as a classifier and as a generator of enriched text representations within a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) framework. We evaluate these methods by comparing them against state-of-the-art approaches, revealing LLAMA's potential to significantly improve classification outcomes by providing deep contextual insights into medical texts. | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | 507,286 |
2312.15877 | PBCounter: Weighted Model Counting on Pseudo-Boolean Formulas | In Weighted Model Counting (WMC), we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its literals. The current WMC solvers work on Conjunctive Normal Form (CNF) formulas. However, CNF is not a natural representation for human-being in many applications. Motivated by the stronger expressive power of pseudo-Boolean (PB) formulas than CNF, we propose to perform WMC on PB formulas. Based on a recent dynamic programming algorithm framework called ADDMC for WMC, we implement a weighted PB counting tool PBCounter. We compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD, ExactMC, D4, and ADDMC, where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF formula. The experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 418,172 |
2307.09153 | OPHAvatars: One-shot Photo-realistic Head Avatars | We propose a method for synthesizing photo-realistic digital avatars from only one portrait as the reference. Given a portrait, our method synthesizes a coarse talking head video using driving keypoints features. And with the coarse video, our method synthesizes a coarse talking head avatar with a deforming neural radiance field. With rendered images of the coarse avatar, our method updates the low-quality images with a blind face restoration model. With updated images, we retrain the avatar for higher quality. After several iterations, our method can synthesize a photo-realistic animatable 3D neural head avatar. The motivation of our method is deformable neural radiance field can eliminate the unnatural distortion caused by the image2video method. Our method outperforms state-of-the-art methods in quantitative and qualitative studies on various subjects. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 380,071 |
2210.07413 | Invariance-adapted decomposition and Lasso-type contrastive learning | Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the mechanism that describes this effectiveness have not been thoroughly analyzed, and many studies have been conducted to investigate the data structures captured by contrastive learning. In particular, the recent study of \citet{content_isolate} has shown that contrastive learning is capable of decomposing the data space into the space that is invariant to all augmentations and its complement. In this paper, we introduce the notion of invariance-adapted latent space that decomposes the data space into the intersections of the invariant spaces of each augmentation and their complements. This decomposition generalizes the one introduced in \citet{content_isolate}, and describes a structure that is analogous to the frequencies in the harmonic analysis of a group. We experimentally show that contrastive learning with lasso-type metric can be used to find an invariance-adapted latent space, thereby suggesting a new potential for the contrastive learning. We also investigate when such a latent space can be identified up to mixings within each component. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 323,691 |
1908.05786 | TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for
Video Saliency Detection | TASED-Net is a 3D fully-convolutional network architecture for video saliency detection. It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information. As a result, a single prediction map is produced from an input clip of multiple frames. Frame-wise saliency maps can be predicted by applying TASED-Net in a sliding-window fashion to a video. The proposed approach assumes that the saliency map of any frame can be predicted by considering a limited number of past frames. The results of our extensive experiments on video saliency detection validate this assumption and demonstrate that our fully-convolutional model with temporal aggregation method is effective. TASED-Net significantly outperforms previous state-of-the-art approaches on all three major large-scale datasets of video saliency detection: DHF1K, Hollywood2, and UCFSports. After analyzing the results qualitatively, we observe that our model is especially better at attending to salient moving objects. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 141,811 |
2007.06286 | Beyond Graph Neural Networks with Lifted Relational Neural Networks | We demonstrate a declarative differentiable programming framework based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to encode relational learning scenarios. When presented with relational data, such as various forms of graphs, the program interpreter dynamically unfolds differentiable computational graphs to be used for the program parameter optimization by standard means. Following from the used declarative Datalog abstraction, this results into compact and elegant learning programs, in contrast with the existing procedural approaches operating directly on the computational graph level. We illustrate how this idea can be used for an efficient encoding of a diverse range of existing advanced neural architectures, with a particular focus on Graph Neural Networks (GNNs). Additionally, we show how the contemporary GNN models can be easily extended towards higher relational expressiveness. In the experiments, we demonstrate correctness and computation efficiency through comparison against specialized GNN deep learning frameworks, while shedding some light on the learning performance of existing GNN models. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | true | 186,969 |
2402.10949 | The Unreasonable Effectiveness of Eccentric Automatic Prompts | Large Language Models (LLMs) have demonstrated remarkable problem-solving and basic mathematics abilities. However, their efficacy is highly contingent on the formulation of the prompt. This study endeavors to quantify the influence of incorporating "positive thinking" into the system message of the prompt, then compare that to systematic prompt optimization. We assess the performance of 60 combinations of system message snippets, tested with and without Chain of Thought prompting, across three models with parameters ranging from 7 to 70 billion on the GSM8K dataset. Our findings reveal that results do not universally generalize across models. In most instances, the inclusion of "positive thinking" prompts positively affected model performance. Notably, however, Llama2-70B exhibited an exception when not utilizing Chain of Thought, as the optimal system message was found to be none at all. Given the combinatorial complexity, and thus computation time, of experimenting with hand-tuning prompts for large black-box models, we then compared the performance of the best "positive thinking" prompt against the output of systematic prompt optimization. We show that employing an automated prompt optimizer emerges as the most effective method for enhancing performance, even when working with smaller open-source models. Additionally, our findings reveal that the highest-scoring, automatically-optimized prompt exhibits a degree of peculiarity far beyond expectations. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 430,178 |
2111.06907 | Improving Experience Replay through Modeling of Similar Transitions'
Sets | In this work, we propose and evaluate a new reinforcement learning method, COMPact Experience Replay (COMPER), which uses temporal difference learning with predicted target values based on recurrence over sets of similar transitions, and a new approach for experience replay based on two transitions memories. Our objective is to reduce the required number of experiences to agent training regarding the total accumulated rewarding in the long run. Its relevance to reinforcement learning is related to the small number of observations that it needs to achieve results similar to that obtained by relevant methods in the literature, that generally demand millions of video frames to train an agent on the Atari 2600 games. We report detailed results from five training trials of COMPER for just 100,000 frames and about 25,000 iterations with a small experiences memory on eight challenging games of Arcade Learning Environment (ALE). We also present results for a DQN agent with the same experimental protocol on the same games set as the baseline. To verify the performance of COMPER on approximating a good policy from a smaller number of observations, we also compare its results with that obtained from millions of frames presented on the benchmark of ALE. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 266,208 |
2009.02562 | Permutation-equivariant and Proximity-aware Graph Neural Networks with
Stochastic Message Passing | Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. Both properties are needed to tackle some challenging graph problems, such as finding communities and leaders. In this paper, we first analytically show that the existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. Then, we propose Stochastic Message Passing (SMP) model, a general and simple GNN to maintain both proximity-awareness and permutation-equivariance. In order to preserve node proximities, we augment the existing GNNs with stochastic node representations. We theoretically prove that the mechanism can enable GNNs to preserve node proximities, and at the same time, maintain permutation-equivariance with certain parametrization. We report extensive experimental results on ten datasets and demonstrate the effectiveness and efficiency of SMP for various typical graph mining tasks, including graph reconstruction, node classification, and link prediction. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 194,577 |
2410.12707 | FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs
with Adaptive Compression | To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system designed and implemented for training DNNs using geo-distributed GPUs across different computing clusters or individual devices. Decentralized training faces significant challenges regarding system design and efficiency, including: 1) the need for remote automatic differentiation (RAD), 2) support for flexible model definitions and heterogeneous software, 3) heterogeneous hardware leading to low resource utilization or the straggler problem, and 4) slow network communication. To address these challenges, in the system design, we represent the model as a directed acyclic graph of operators (OP-DAG). Each node in the DAG represents the operator in the DNNs, while the edge represents the data dependency between operators. Based on this design, 1) users are allowed to customize any DNN without caring low-level operator implementation; 2) we enable the task scheduling with the more fine-grained sub-tasks, offering more optimization space; 3) a DAG runtime executor can implement RAD withour requiring the consistent low-level ML framework versions. To enhance system efficiency, we implement a workload estimator and design an OP-Fence scheduler to cluster devices with similar bandwidths together and partition the DAG to increase throughput. Additionally, we propose an AdaTopK compressor to adaptively compress intermediate activations and gradients at the slowest communication links. To evaluate the convergence and efficiency of our system and algorithms, we train ResNet-101 and GPT-2 on three real-world testbeds using 48 GPUs connected with 8 Mbps~10 Gbps networks. Experimental results demonstrate that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 499,155 |
2408.14434 | Employing Artificial Intelligence to Steer Exascale Workflows with
Colmena | Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 483,530 |
2403.16852 | Towards Explainability in Legal Outcome Prediction Models | Current legal outcome prediction models - a staple of legal NLP - do not explain their reasoning. However, to employ these models in the real world, human legal actors need to be able to understand the model's decisions. In the case of common law, legal practitioners reason towards the outcome of a case by referring to past case law, known as precedent. We contend that precedent is, therefore, a natural way of facilitating explainability for legal NLP models. In this paper, we contribute a novel method for identifying the precedent employed by legal outcome prediction models. Furthermore, by developing a taxonomy of legal precedent, we are able to compare human judges and neural models with respect to the different types of precedent they rely on. We find that while the models learn to predict outcomes reasonably well, their use of precedent is unlike that of human judges. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 441,216 |
1901.07744 | Automated Essay Scoring based on Two-Stage Learning | Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e.g. the essays composed of permuted sentences and the prompt-irrelevant essays. Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which integrates the advantages of both feature-engineered and end-to-end AES models. In experiments, we compare TSLF against a number of strong baselines, and the results demonstrate the effectiveness and robustness of our models. TSLF surpasses all the baselines on five-eighths of prompts and achieves new state-of-the-art average performance when without negative samples. After adding some adversarial essays to the original datasets, TSLF outperforms the feature-engineered and end-to-end baselines to a great extent, and shows great robustness. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 119,288 |
2407.17949 | Fast convergence of the Expectation Maximization algorithm under a
logarithmic Sobolev inequality | By utilizing recently developed tools for constructing gradient flows on Wasserstein spaces, we extend an analysis technique commonly employed to understand alternating minimization algorithms on Euclidean space to the Expectation Maximization (EM) algorithm via its representation as coordinate-wise minimization on the product of a Euclidean space and a space of probability distributions due to Neal and Hinton (1998). In so doing we obtain finite sample error bounds and exponential convergence of the EM algorithm under a natural generalisation of a log-Sobolev inequality. We further demonstrate that the analysis technique is sufficiently flexible to allow also the analysis of several variants of the EM algorithm. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 476,187 |
2410.19400 | Offline Reinforcement Learning with OOD State Correction and OOD Action
Suppression | In offline reinforcement learning (RL), addressing the out-of-distribution (OOD) action issue has been a focus, but we argue that there exists an OOD state issue that also impairs performance yet has been underexplored. Such an issue describes the scenario when the agent encounters states out of the offline dataset during the test phase, leading to uncontrolled behavior and performance degradation. To this end, we propose SCAS, a simple yet effective approach that unifies OOD state correction and OOD action suppression in offline RL. Technically, SCAS achieves value-aware OOD state correction, capable of correcting the agent from OOD states to high-value in-distribution states. Theoretical and empirical results show that SCAS also exhibits the effect of suppressing OOD actions. On standard offline RL benchmarks, SCAS achieves excellent performance without additional hyperparameter tuning. Moreover, benefiting from its OOD state correction feature, SCAS demonstrates enhanced robustness against environmental perturbations. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 502,297 |
2204.06319 | A "parallel universe" scheme for crack nucleation in the phase field
approach to fracture | Crack nucleation is crucial in many industrial applications. The phase field method for fracture transforms the crack nucleation problem into a minimization problem of the sum of the elastic potential energy and the crack surface energy. Due to the polyconvexity of the formulation, starting from a crackless solid, a standard Newton iteration may lead to a solution with no crack, even though a cracked solution has a lower total energy. As such, the critical load for cracking is highly overestimated. Here, we propose an algorithm termed "parallel universe" algorithm to capture the global minimum. This algorithm has two key ingredients: (a) a necessary condition for cracking solely based on the current crackless solution, and (b) beginning from when this condition is met, Newton iteration with two initial guesses, a crackles one and a cracked one, will both be performed and the converged candidate solution with lower energy is accepted as the solution at that load step. Once the cracked candidate solution is accepted, the crackless one is discarded, i.e., only one universe is retained. This cracked initial guess is obtained only once for all load steps by solving a series of similar minimization problems with a progressively reduced critical crack energy release rate. Numerical examples with isotropic and anisotropic critical crack energy release rates indicate that the proposed algorithm is more reliable (as there is no need to retrace) and more efficient than the standard Newton iteration and a well-known backtracking algorithm. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 291,305 |
1101.3098 | Quantum Convex Support | Convex support, the mean values of a set of random variables, is central in information theory and statistics. Equally central in quantum information theory are mean values of a set of observables in a finite-dimensional C*-algebra A, which we call (quantum) convex support. The convex support can be viewed as a projection of the state space of A and it is a projection of a spectrahedron. Spectrahedra are increasingly investigated at least since the 1990's boom in semidefinite programming. We recall the geometry of the positive semi-definite cone and of the state space. We write a convex duality for general self-dual convex cones. This restricts to projections of state spaces and connects them to results on spectrahedra. Really new in this article is an analysis of the face lattice of convex support by mapping this lattice to a lattice of orthogonal projections, using natural isomorphisms. The result encodes the face lattice of the convex support into a set of projections in A and enables the integration of convex geometry with matrix calculus or algebraic techniques. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 8,830 |
2003.09234 | DeepFake Detection: Current Challenges and Next Steps | High quality fake videos and audios generated by AI-algorithms (the deep fakes) have started to challenge the status of videos and audios as definitive evidence of events. In this paper, we highlight a few of these challenges and discuss the research opportunities in this direction. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 168,992 |
1804.01983 | High-dimension Tensor Completion via Gradient-based Optimization Under
Tensor-train Format | Tensor train (TT) decomposition has drawn people's attention due to its powerful representation ability and performance stability in high-order tensors. In this paper, we propose a novel approach to recover the missing entries of incomplete data represented by higher-order tensors. We attempt to find the low-rank TT decomposition of the incomplete data which captures the latent features of the whole data and then reconstruct the missing entries. By applying gradient descent algorithms, tensor completion problem is efficiently solved by optimization models. We propose two TT-based algorithms: Tensor Train Weighted Optimization (TT-WOPT) and Tensor Train Stochastic Gradient Descent (TT-SGD) to optimize TT decomposition factors. In addition, a method named Visual Data Tensorization (VDT) is proposed to transform visual data into higher-order tensors, resulting in the performance improvement of our algorithms. The experiments in synthetic data and visual data show high efficiency and performance of our algorithms compared to the state-of-the-art completion algorithms, especially in high-order, high missing rate, and large-scale tensor completion situations. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | true | 94,321 |
2206.13829 | Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake
Image Detection | Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society. The continual emergence of new and varied techniques brings with it a further problem to be faced, namely the ability of deepfake detection models to update themselves promptly in order to be able to identify manipulations carried out using even the most recent methods. This is an extremely complex problem to solve, as training a model requires large amounts of data, which are difficult to obtain if the deepfake generation method is too recent. Moreover, continuously retraining a network would be unfeasible. In this paper, we ask ourselves if, among the various deep learning techniques, there is one that is able to generalise the concept of deepfake to such an extent that it does not remain tied to one or more specific deepfake generation methods used in the training set. We compared a Vision Transformer with an EfficientNetV2 on a cross-forgery context based on the ForgeryNet dataset. From our experiments, It emerges that EfficientNetV2 has a greater tendency to specialize often obtaining better results on training methods while Vision Transformers exhibit a superior generalization ability that makes them more competent even on images generated with new methodologies. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 305,094 |
2407.02867 | Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive
Multimodal Knowledge Graph Completion | A large number of studies have emerged for Multimodal Knowledge Graph Completion (MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed to study the inductive MKGC (IMKGC) involving emerging entities unseen during training. Existing inductive approaches focus on learning textual entity representations, which neglect rich semantic information in visual modality. Moreover, they focus on aggregating structural neighbors from existing KGs, which of emerging entities are usually limited. However, the semantic neighbors are decoupled from the topology linkage and usually imply the true target entity. In this paper, we propose the IMKGC task and a semantic neighbor retrieval-enhanced IMKGC framework CMR, where the contrast brings the helpful semantic neighbors close, and then the memorize supports semantic neighbor retrieval to enhance inference. Specifically, we first propose a unified cross-modal contrastive learning to simultaneously capture the textual-visual and textual-textual correlations of query-entity pairs in a unified representation space. The contrastive learning increases the similarity of positive query-entity pairs, therefore making the representations of helpful semantic neighbors close. Then, we explicitly memorize the knowledge representations to support the semantic neighbor retrieval. At test time, we retrieve the nearest semantic neighbors and interpolate them to the query-entity similarity distribution to augment the final prediction. Extensive experiments validate the effectiveness of CMR on three inductive MKGC datasets. Codes are available at https://github.com/OreOZhao/CMR. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | 469,919 |
2411.10902 | Attention-based U-Net Method for Autonomous Lane Detection | Lane detection involves identifying lanes on the road and accurately determining their location and shape. This is a crucial technique for modern assisted and autonomous driving systems. However, several unique properties of lanes pose challenges for detection methods. The lack of distinctive features can cause lane detection algorithms to be confused by other objects with similar appearances. Additionally, the varying number of lanes and the diversity in lane line patterns, such as solid, broken, single, double, merging, and splitting lines, further complicate the task. To address these challenges, Deep Learning (DL) approaches can be employed in various ways. Merging DL models with an attention mechanism has recently surfaced as a new approach. In this context, two deep learning-based lane recognition methods are proposed in this study. The first method employs the Feature Pyramid Network (FPN) model, delivering an impressive 87.59% accuracy in detecting road lanes. The second method, which incorporates attention layers into the U-Net model, significantly boosts the performance of semantic segmentation tasks. The advanced model, achieving an extraordinary 98.98% accuracy and far surpassing the basic U-Net model, clearly showcases its superiority over existing methods in a comparative analysis. The groundbreaking findings of this research pave the way for the development of more effective and reliable road lane detection methods, significantly advancing the capabilities of modern assisted and autonomous driving systems. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 508,838 |
2007.01498 | Temporal-Logic-Based Reward Shaping for Continuing Reinforcement
Learning Tasks | In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for average-reward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 185,450 |
2304.02870 | Protecting User Privacy in Online Settings via Supervised Learning | Companies that have an online presence-in particular, companies that are exclusively digital-often subscribe to this business model: collect data from the user base, then expose the data to advertisement agencies in order to turn a profit. Such companies routinely market a service as "free", while obfuscating the fact that they tend to "charge" users in the currency of personal information rather than money. However, online companies also gather user data for more principled purposes, such as improving the user experience and aggregating statistics. The problem is the sale of user data to third parties. In this work, we design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user. In our evaluation, we collect a dataset of network requests and measure the performance of several classifiers that adhere to the supervised learning paradigm. The results of our evaluation demonstrate the feasibility and potential of our approach. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 356,588 |
0709.2330 | Queueing for ergodic arrivals and services | In this paper we revisit the results of Loynes (1962) on stability of queues for ergodic arrivals and services, and show examples when the arrivals are bounded and ergodic, the service rate is constant, and under stability the limit distribution has larger than exponential tail. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 660 |
2412.06845 | Fully Open Source Moxin-7B Technical Report | Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community due to their remarkable performance and versatility. Simultaneously, open-source LLMs, such as LLaMA and Mistral, have made great contributions to the ever-increasing popularity of LLMs due to the ease to customize and deploy the models across diverse applications. Although open-source LLMs present unprecedented opportunities for innovation and research, the commercialization of LLMs has raised concerns about transparency, reproducibility, and safety. Many open-source LLMs fail to meet fundamental transparency requirements by withholding essential components like training code and data, and some use restrictive licenses whilst claiming to be "open-source," which may hinder further innovations on LLMs. To mitigate this issue, we introduce Moxin 7B, a fully open-source LLM developed in accordance with the Model Openness Framework (MOF), a ranked classification system that evaluates AI models based on model completeness and openness, adhering to principles of open science, open source, open data, and open access. Our model achieves the highest MOF classification level of "open science" through the comprehensive release of pre-training code and configurations, training and fine-tuning datasets, and intermediate and final checkpoints. Experiments show that our model achieves superior performance in zero-shot evaluation compared with popular 7B models and performs competitively in few-shot evaluation. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 515,413 |
2402.10649 | Hermite Neural Network Simulation for Solving the 2D Schrodinger
Equation | The Schrodinger equation is a mathematical equation describing the wave function's behavior in a quantum-mechanical system. It is a partial differential equation that provides valuable insights into the fundamental principles of quantum mechanics. In this paper, the aim was to solve the Schrodinger equation with sufficient accuracy by using a mixture of neural networks with the collocation method base Hermite functions. Initially, the Hermite functions roots were employed as collocation points, enhancing the efficiency of the solution. The Schrodinger equation is defined in an infinite domain, the use of Hermite functions as activation functions resulted in excellent precision. Finally, the proposed method was simulated using MATLAB's Simulink tool. The results were then compared with those obtained using Physics-informed neural networks and the presented method. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | true | 430,051 |
2212.09621 | Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document
Understanding | Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability. | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | 337,170 |
1304.3209 | Improvement studies on neutron-gamma separation in HPGe detectors by
using neural networks | The neutrons emitted in heavy-ion fusion-evaporation (HIFE) reactions together with the gamma-rays cause unwanted backgrounds in gamma-ray spectra. Especially in the nuclear reactions, where relativistic ion beams (RIBs) are used, these neutrons are serious problem. They have to be rejected in order to obtain clearer gamma-ray peaks. In this study, the radiation energy and three criteria which were previously determined for separation between neutron and gamma-rays in the HPGe detectors have been used in artificial neural network (ANN) for improving of the decomposition power. According to the preliminary results obtained from ANN method, the ratio of neutron rejection has been improved by a factor of 1.27 and the ratio of the lost in gamma-rays has been decreased by a factor of 0.50. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 23,844 |
2403.18839 | Long Short-Term Memory Pattern Recognition in Currency Trading | This study delves into the analysis of financial markets through the lens of Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th century. Focusing on the accumulation pattern within the Wyckoff framework, the research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics and identifying potential trading opportunities. By dissecting the intricacies of these phases, the study sheds light on the creation of liquidity through market structure, offering insights into how traders can leverage this knowledge to anticipate price movements and make informed decisions. The effective detection and analysis of Wyckoff patterns necessitate robust computational models capable of processing complex market data, with spatial data best analyzed using Convolutional Neural Networks (CNNs) and temporal data through Long Short-Term Memory (LSTM) models. The creation of training data involves the generation of swing points, representing significant market movements, and filler points, introducing noise and enhancing model generalization. Activation functions, such as the sigmoid function, play a crucial role in determining the output behavior of neural network models. The results of the study demonstrate the remarkable efficacy of deep learning models in detecting Wyckoff patterns within financial data, underscoring their potential for enhancing pattern recognition and analysis in financial markets. In conclusion, the study highlights the transformative potential of AI-driven approaches in financial analysis and trading strategies, with the integration of AI technologies shaping the future of trading and investment practices. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 442,101 |
2410.14282 | You Only Look Twice! for Failure Causes Identification of Drill Bits | Efficient identification of the root causes of drill bit failure is crucial due to potential impacts such as operational losses, safety threats, and delays. Early recognition of these failures enables proactive maintenance, reducing risks and financial losses associated with unforeseen breakdowns and prolonged downtime. Thus, our study investigates various causes of drill bit failure using images of different blades. The process involves annotating cutters with their respective locations and damage types, followed by the development of two YOLO Location and Damage Cutter Detection models, as well as multi-class multi-label Decision Tree and Random Forests models to identify the causes of failure by assessing the cutters' location and damage type. Additionally, RRFCI is proposed for the classification of failure causes. Notably, the cutter location detection model achieved a high score of 0.97 mPA, and the cutter damage detection model yielded a 0.49 mPA. The rule-based approach over-performed both DT and RF in failure cause identification, achieving a macro-average F1-score of 0.94 across all damage causes. The integration of the complete automated pipeline successfully identified 100\% of the 24 failure causes when tested on independent sets of ten drill bits, showcasing its potential to efficiently assist experts in identifying the root causes of drill bit damages. | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 499,971 |
2501.16398 | Visualizing the Local Atomic Environment Features of Machine Learning
Interatomic Potential | This paper addresses the challenges of creating efficient and high-quality datasets for machine learning potential functions. We present a novel approach, termed DV-LAE (Difference Vectors based on Local Atomic Environments), which utilizes the properties of atomic local environments and employs histogram statistics to generate difference vectors. This technique facilitates dataset screening and optimization, effectively minimizing redundancy while maintaining data diversity. We have validated the optimized datasets in high-temperature and high-pressure hydrogen systems as well as the {\alpha}-Fe/H binary system, demonstrating a significant reduction in computational resource usage without compromising prediction accuracy. Additionally, our method has revealed new structures that emerge during simulations but were underrepresented in the initial training datasets. The redundancy in the datasets and the distribution of these new structures can be visually analyzed through the visualization of difference vectors. This approach enhances our understanding of the characteristics of these newly formed structures and their impact on physical processes. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 527,960 |
1812.03239 | Communication-Efficient Policy Gradient Methods for Distributed
Reinforcement Learning | This paper deals with distributed policy optimization in reinforcement learning, which involves a central controller and a group of learners. In particular, two typical settings encountered in several applications are considered: multi-agent reinforcement learning (RL) and parallel RL, where frequent information exchanges between the learners and the controller are required. For many practical distributed systems, however, the overhead caused by these frequent communication exchanges is considerable, and becomes the bottleneck of the overall performance. To address this challenge, a novel policy gradient approach is developed for solving distributed RL. The novel approach adaptively skips the policy gradient communication during iterations, and can reduce the communication overhead without degrading learning performance. It is established analytically that: i) the novel algorithm has convergence rate identical to that of the plain-vanilla policy gradient; while ii) if the distributed learners are heterogeneous in terms of their reward functions, the number of communication rounds needed to achieve a desirable learning accuracy is markedly reduced. Numerical experiments corroborate the communication reduction attained by the novel algorithm compared to alternatives. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | false | false | false | 115,958 |
1809.10089 | Residuum-Condition Diagram and Reduction of Over-Complete Endmember-Sets | Extracting reference spectra, or endmembers (EMs) from a given multi- or hyperspectral image, as well as estimating the size of the EM set, plays an important role in multispectral image processing. In this paper, we present condition-residuum-diagrams. By plotting the residuum resulting from the unmixing and reconstruction and the condition number of various EM sets, the resulting diagram provides insight into the behavior of the spectral unmixing under a varying amount of endmembers (EMs). Furthermore, we utilize condition-residuum-diagrams to realize an EM reduction algorithm that starts with an initially extracted, over-complete EM set. An over-complete EM set commonly exhibits a good unmixing result, i.e. a lower reconstruction residuum, but due to its partial redundancy, the unmixing gets numerically unstable, i.e. the unmixed abundances values are less reliable. Our greedy reduction scheme improves the EM set by reducing the condition number, i.e. enhancing the set's stability, while keeping the reconstruction error as low as possible. The resulting set sequence gives hint to the optimal EM set and its size. We demonstrate the benefit of our condition-residuum-diagram and reduction scheme on well-studied datasets with known reference EM set sizes for several well-known EE algorithms. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 108,819 |
2412.04903 | EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation | Multimodal large language models (MLLMs) have achieved remarkable progress on various visual question answering and reasoning tasks leveraging instruction fine-tuning specific datasets. They can also learn from preference data annotated by human to enhance their reasoning ability and mitigate hallucinations. Most of preference data is generated from the model itself. However, existing methods require high-quality critical labels, which are costly and rely on human or proprietary models like GPT-4V. In this work, we propose Enhancing Alignment in MLLMs via Critical Observation (EACO), which aligns MLLMs by self-generated preference data using only 5k images economically. Our approach begins with collecting and refining a Scoring Evaluation Instruction-tuning dataset to train a critical evaluation model, termed the Critic. This Critic observes model responses across multiple dimensions, selecting preferred and non-preferred outputs for refined Direct Preference Optimization (DPO) tuning. To further enhance model performance, we employ an additional supervised fine-tuning stage after preference tuning. EACO reduces the overall hallucinations by 65.6% on HallusionBench and improves the reasoning ability by 21.8% on MME-Cognition. EACO achieves an 8.5% improvement over LLaVA-v1.6-Mistral-7B across multiple benchmarks. Remarkably, EACO also shows the potential critical ability in open-source MLLMs, demonstrating that EACO is a viable path to boost the competence of MLLMs. | false | false | false | false | true | false | true | false | true | false | false | true | false | false | false | false | false | false | 514,620 |
2406.18628 | IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware
Underwater Image Enhancement | Underwater image quality is affected by fluorescence, low illumination, absorption, and scattering. Recent works in underwater image enhancement have proposed different deep network architectures to handle these problems. Most of these works have proposed a single network to handle all the challenges. We believe that deep networks trained for specific conditions deliver better performance than a single network learned from all degradation cases. Accordingly, the first contribution of this work lies in the proposal of an iterative framework where a single dominant degradation condition is identified and resolved. This proposal considers the following eight degradation conditions -- low illumination, low contrast, haziness, blurred image, presence of noise and color imbalance in three different channels. A deep network is designed to identify the dominant degradation condition. Accordingly, an appropriate deep network is selected for degradation condition-specific enhancement. The second contribution of this work is the construction of degradation condition specific datasets from good quality images of two standard datasets (UIEB and EUVP). This dataset is used to learn the condition specific enhancement networks. The proposed approach is found to outperform nine baseline methods on UIEB and EUVP datasets. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 468,118 |
1909.09459 | Physics-informed semantic inpainting: Application to geostatistical
modeling | A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. To overcome this limitation, here we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 146,264 |
2007.12408 | Quasi-Degradation Probability of Two-User NOMA over Rician Fading
Channels | Non-orthogonal multiple access (NOMA) has a great potential to offer a higher spectral efficiency of multi-user wireless networks than orthogonal multiples access (OMA). Previous work has established the condition, referred to quasi-degradation (QD) probability, under which NOMA has no performance loss compared to the capacity-achieving dirty paper coding for the two-user case. Existing results assume Rayleigh fading channels without line-of-sight (LOS). In many practical scenarios, the channel LOS component is critical to the link quality where the channel gain follows a Rician distribution instead of a Rayleigh distribution. In this work, we analyze the QD probability over multi-input and single-output (MISO) channels subject to Rician fading. The QD probability heavily depends on the angle between two user channels, which involves a matrix quadratic form in random vectors and a stochastic matrix. With the deterministic LOS component, the distribution of the matrix quadratic form is non-central that dramatically complicates the derivation of the QD probability. To remedy this difficulty, a series of approximations is proposed that yields a closed-form expression for the QD probability over MISO Rician channels. Numerical results are presented to assess the analysis accuracy and get insights into the optimality of NOMA over Rician fading channels. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 188,814 |
2108.10316 | A Generalization of the ASR Search Algorithm to 2-Generator
Quasi-Twisted Codes | One of the main goals of coding theory is to construct codes with best possible parameters and properties. A special class of codes called quasi-twisted (QT) codes is well-known to produce codes with good parameters. Most of the work on QT codes has been over the 1-generator case. In this work, we focus on 2-generator QT codes and generalize the ASR algorithm that has been very effective to produce new linear codes from 1-generator QT codes. Moreover, we also generalize a recent algorithm to test equivalence of cyclic codes to constacyclic codes. This algorithm makes the ASR search even more effective. As a result of implementing our algorithm, we have found 103 QT codes that are new among the class of QT codes. Additionally, most of these codes possess the following additional properties: a) they have the same parameters as best known linear codes, and b) many of the have additional desired properties such as being LCD and dual-containing. Further, we have also found a binary 2-generator QT code that is new (record breaking) among all binary linear codes and its extension yields another record breaking binary linear code. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 251,864 |
2406.02016 | Adaptive and Optimal Second-order Optimistic Methods for Minimax
Optimization | We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems. By means of an adaptive step size, our algorithms feature a simple update rule that requires solving only one linear system per iteration, eliminating the need for line search or backtracking mechanisms. Specifically, we base our algorithms on the optimistic method and appropriately combine it with second-order information. Moreover, distinct from common adaptive schemes, we define the step size recursively as a function of the gradient norm and the prediction error in the optimistic update. We first analyze a variant where the step size requires knowledge of the Lipschitz constant of the Hessian. Under the additional assumption of Lipschitz continuous gradients, we further design a parameter-free version by tracking the Hessian Lipschitz constant locally and ensuring the iterates remain bounded. We also evaluate the practical performance of our algorithm by comparing it to existing second-order algorithms for minimax optimization. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 460,576 |
2211.05077 | Prompting Large Pre-trained Vision-Language Models For Compositional
Concept Learning | This work explores the zero-shot compositional learning ability of large pre-trained vision-language models(VLMs) within the prompt-based learning framework and propose a model (\textit{PromptCompVL}) to solve the compositonal zero-shot learning (CZSL) problem. \textit{PromptCompVL} makes two design choices: first, it uses a soft-prompting instead of hard-prompting to inject learnable parameters to reprogram VLMs for compositional learning. Second, to address the compositional challenge, it uses the soft-embedding layer to learn primitive concepts in different combinations. By combining both soft-embedding and soft-prompting, \textit{PromptCompVL} achieves state-of-the-art performance on the MIT-States dataset. Furthermore, our proposed model achieves consistent improvement compared to other CLIP-based methods which shows the effectiveness of the proposed prompting strategies for CZSL. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 329,432 |
1311.5204 | On Quantifying Qualitative Geospatial Data: A Probabilistic Approach | Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships. To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based maps visualizing the spatial extent of the various relations. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | 28,554 |
1805.04680 | AdvEntuRe: Adversarial Training for Textual Entailment with
Knowledge-Guided Examples | We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it. First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via only a handful of rule templates. Second, to make the entailment model - a discriminator - more robust, we propose the first GAN-style approach for training it using a natural language example generator that iteratively adjusts based on the discriminator's performance. We demonstrate effectiveness using two entailment datasets, where the proposed methods increase accuracy by 4.7% on SciTail and by 2.8% on a 1% training sub-sample of SNLI. Notably, even a single hand-written rule, negate, improves the accuracy on the negation examples in SNLI by 6.1%. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 97,284 |
1809.03457 | Event Graphs: Advances and Applications of Second-Order Time-Unfolded
Temporal Network Models | Recent advances in data collection and storage have allowed both researchers and industry alike to collect data in real time. Much of this data comes in the form of 'events', or timestamped interactions, such as email and social media posts, website clickstreams, or protein-protein interactions. This of type data poses new challenges for modelling, especially if we wish to preserve all temporal features and structure. We propose a generalised framework to explore temporal networks using second-order time-unfolded models, called event graphs. Through examples we demonstrate how event graphs can be used to understand the higher-order topological-temporal structure of temporal networks and capture properties of the network that are unobserved when considering either a static (or time-aggregated) model. Furthermore, we show that by modelling a temporal network as an event graph our analysis extends easily to consider non-dyadic interactions, known as hyper-events. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 107,332 |
2402.03384 | Survival and grade of the glioma prediction using transfer learning | Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3 to 6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 426,995 |
2205.03137 | Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype
Learning | Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning. Existing approaches mainly focus on exploiting manifold and pseudo-labeling to make use of large unlabeled data points. A fundamental challenge here lies in the large intra-class variations of local geometric structure, resulting in subclasses within a semantic class. In this work, we leverage this intuition and opt for maintaining an individual classifier for each subclass. Technically, we design a multi-prototype classifier, each prototype serves as the classifier weights for one subclass. To enable effective updating of multi-prototype classifier weights, we propose two constraints respectively for updating the prototypes w.r.t. all point features and for encouraging the learning of diverse prototypes. Experiments on weakly supervised 3D point cloud segmentation tasks validate the efficacy of proposed method in particular at low-label regime. Our hypothesis is also verified given the consistent discovery of semantic subclasses at no cost of additional annotations. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 295,181 |
2305.04461 | Locally Attentional SDF Diffusion for Controllable 3D Shape Generation | Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and control the local geometry of generated shapes. To address these challenges, we propose a diffusion-based 3D generation framework -- locally attentional SDF diffusion, to model plausible 3D shapes, via 2D sketch image input. Our method is built on a two-stage diffusion model. The first stage, named occupancy-diffusion, aims to generate a low-resolution occupancy field to approximate the shape shell. The second stage, named SDF-diffusion, synthesizes a high-resolution signed distance field within the occupied voxels determined by the first stage to extract fine geometry. Our model is empowered by a novel view-aware local attention mechanism for image-conditioned shape generation, which takes advantage of 2D image patch features to guide 3D voxel feature learning, greatly improving local controllability and model generalizability. Through extensive experiments in sketch-conditioned and category-conditioned 3D shape generation tasks, we validate and demonstrate the ability of our method to provide plausible and diverse 3D shapes, as well as its superior controllability and generalizability over existing work. Our code and trained models are available at https://zhengxinyang.github.io/projects/LAS-Diffusion.html | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 362,777 |
2412.20185 | Pushing the Envelope of Low-Bit LLM via Dynamic Error Compensation | Quantization of Large Language Models (LLMs) has recently gained popularity, particularly for on-device settings with limited hardware resources. While efficient, quantization inevitably degrades model quality, especially in aggressive low-bit settings such as 3-bit and 4-bit precision. In this paper, we propose QDEC, an inference scheme that improves the quality of low-bit LLMs while preserving the key benefits of quantization: GPU memory savings and inference latency reduction. QDEC stores the residual matrix -- the difference between full-precision and quantized weights -- in CPU, and dynamically fetches the residuals for only a small portion of the weights. This portion corresponds to the salient channels, marked by activation outliers, with the fetched residuals helping to correct quantization errors in these channels. Salient channels are identified dynamically at each decoding step by analyzing the input activations -- this allows for the adaptation to the dynamic nature of activation distribution, and thus maximizes the effectiveness of error compensation. We demonstrate the effectiveness of QDEC by augmenting state-of-the-art quantization methods. For example, QDEC reduces the perplexity of a 3-bit Llama-3-8B-Instruct model from 10.15 to 9.12 -- outperforming its 3.5-bit counterpart -- while adding less than 0.0003\% to GPU memory usage and incurring only a 1.7\% inference slowdown on NVIDIA RTX 4050 Mobile GPU. The code will be publicly available soon. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 521,124 |
2108.10585 | Learning Spatiotemporal Occupancy Grid Maps for Lifelong Navigation in
Dynamic Scenes | We present a novel method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future information of dynamic scenes. Our automated generation process creates groundtruth SOGMs from previous navigation data. We build on prior work to annotate lidar points based on their dynamic properties, which are then projected on time-stamped 2D grids: SOGMs. We design a 3D-2D feedforward architecture, trained to predict the future time steps of SOGMs, given 3D lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for robots. The network is composed of a 3D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2D front-end that predicts the future information embedded in the SOGMs within planning. We also design a navigation pipeline that uses these predicted SOGMs. We provide both quantitative and qualitative insights into the predictions and validate our choices of network design with a comparison to the state of the art and ablation studies. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 251,945 |
1811.09384 | Online Learning for Network Constrained Demand Response Pricing in
Distribution Systems | Flexible demand response (DR) resources can be leveraged to accommodate the stochasticity of some distributed energy resources. This paper develops an online learning approach that continuously estimates price sensitivities of residential DR participants and produces such price signals to the DR participants that ensure a desired level of DR capacity. The proposed learning approach incorporates the dispatch decisions on DR resources into the distributionally robust chance-constrained optimal power flow (OPF) framework. This integration is shown to adequately remunerate DR resources and co-optimize the dispatch of DR and conventional generation resources. The distributionally robust chance-constrained formulation only relies on empirical data acquired over time and makes no restrictive assumptions on the underlying distribution of the demand uncertainty. The distributional robustness also allows for robustifying the optimal solution against systematically misestimating empirically learned parameters. The effectiveness of the proposed learning approach is shown via numerical experiments. The paper is accompanied by the code and data supplement released for public use, see [27]. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 114,237 |
2005.13178 | Generative Adversarial Networks (GANs): An Overview of Theoretical
Model, Evaluation Metrics, and Recent Developments | One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF). In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously. The generator learns to generate plausible data, and the discriminator learns to distinguish fake data created by the generator from real data samples. Given the rapid growth of GANs over the last few years and their application in various fields, it is necessary to investigate these networks accurately. In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained. Moreover, the most remarkable GAN architectures are categorized and discussed. Finally, the essential applications in computer vision are examined. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 178,935 |
2205.15731 | ViNNPruner: Visual Interactive Pruning for Deep Learning | Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep neural networks to smaller sizes by only decreasing their performance as little as possible. However, such pruning algorithms are often hard to understand by applying them and do not include domain knowledge which can potentially be bad for user goals. We propose ViNNPruner, a visual interactive pruning application that implements state-of-the-art pruning algorithms and the option for users to do manual pruning based on their knowledge. We show how the application facilitates gaining insights into automatic pruning algorithms and semi-automatically pruning oversized networks to make them more efficient using interactive visualizations. | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 299,848 |
1210.7295 | Analysis and Control of Period-Doubling Bifurcation in Buck Converters
Using Harmonic Balance | Period doubling bifurcation in buck converters is studied by using the harmonic balance method. A simple dynamic model of a buck converter in continuous conduction mode under voltage mode or current mode control is derived. This model consists of the feedback connection of a linear system and a nonlinear one. An exact harmonic balance analysis is used to obtain a necessary and sufficient condition for a period doubling bifurcation to occur. If such a bifurcation occurs, the analysis also provides information on its exact location. Using the condition for bifurcation, a feedforward control is designed to eliminate the period doubling bifurcation. This results in a wider range of allowed source voltage, and also in improved line regulation. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 19,422 |
2101.10950 | Asymptotic Supervised Predictive Classifiers under Partition
Exchangeability | The convergence of simultaneous and marginal predictive classifiers under partition exchangeability in supervised classification is obtained. The result shows the asymptotic convergence of these classifiers under infinite amount of training or test data, such that after observing umpteen amount of data, the differences between these classifiers would be negligible. This is an important result from the practical perspective as under the presence of sufficiently large amount of data, one can replace the simpler marginal classifier with computationally more expensive simultaneous one. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 217,109 |
2411.00198 | Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical
Systems | Motivated by the surge of interest in Koopman operator theory, we propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling of unknown, data-driven, nonlinear dynamical systems. This formulation is directly done in an infinite-dimensional space of linear operators or Hilbert space with universal approximation property. The theory of reproducing kernel Hilbert space (RKHS) allows the lifting of nonlinear dynamics to a potentially infinite-dimensional space via linear embeddings, where a general nonlinear function is represented as a set of linear functions or operators in the functional space. This allows us to apply classical linear Bayesian methods such as the Kalman filter directly in the Hilbert space, yielding nonlinear solutions in the original input space. This kernel perspective on the Koopman operator offers two compelling advantages. First, the Hilbert space can be constructed deterministically, agnostic to the nonlinear dynamics. The Gaussian kernel is universal, approximating uniformly an arbitrary continuous target function over any compact domain. Second, Bayesian filter is an adaptive, linear minimum-variance algorithm, allowing the system to update the Koopman operator and continuously track the changes across an extended period of time, ideally suited for modern data-driven applications such as real-time machine learning using streaming data. In this paper, we present several practical implementations to obtain a finite-dimensional approximation of the functional Bayesian filter (FBF). Due to the rapid decay of the Gaussian kernel, excellent approximation is obtained with a small dimension. We demonstrate that this practical approach can obtain accurate results and outperform finite-dimensional Koopman decomposition. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 504,479 |
2310.07598 | Survey on Imbalanced Data, Representation Learning and SEP Forecasting | Deep Learning methods have significantly advanced various data-driven tasks such as regression, classification, and forecasting. However, much of this progress has been predicated on the strong but often unrealistic assumption that training datasets are balanced with respect to the targets they contain. This misalignment with real-world conditions, where data is frequently imbalanced, hampers the effectiveness of such models in practical applications. Methods that reconsider that assumption and tackle real-world imbalances have begun to emerge and explore avenues to address this challenge. One such promising avenue is representation learning, which enables models to capture complex data characteristics and generalize better to minority classes. By focusing on a richer representation of the feature space, these techniques hold the potential to mitigate the impact of data imbalance. In this survey, we present deep learning works that step away from the balanced-data assumption, employing strategies like representation learning to better approximate real-world imbalances. We also highlight a critical application in SEP forecasting where addressing data imbalance is paramount for success. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 399,035 |
2212.05251 | A Unified Knowledge Graph Augmentation Service for Boosting
Domain-specific NLP Tasks | By focusing the pre-training process on domain-specific corpora, some domain-specific pre-trained language models (PLMs) have achieved state-of-the-art results. However, it is under-investigated to design a unified paradigm to inject domain knowledge in the PLM fine-tuning stage. We propose KnowledgeDA, a unified domain language model development service to enhance the task-specific training procedure with domain knowledge graphs. Given domain-specific task texts input, KnowledgeDA can automatically generate a domain-specific language model following three steps: (i) localize domain knowledge entities in texts via an embedding-similarity approach; (ii) generate augmented samples by retrieving replaceable domain entity pairs from two views of both knowledge graph and training data; (iii) select high-quality augmented samples for fine-tuning via confidence-based assessment. We implement a prototype of KnowledgeDA to learn language models for two domains, healthcare and software development. Experiments on domain-specific text classification and QA tasks verify the effectiveness and generalizability of KnowledgeDA. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 335,726 |
2006.07450 | A Unified Learning Platform for Dynamic Frequency Scaling in Pipelined
Processors | A machine learning (ML) design framework is proposed for dynamically adjusting clock frequency based on propagation delay of individual instructions. A Random Forest model is trained to classify propagation delays in real-time, utilizing current operation type, current operands, and computation history as ML features. The trained model is implemented in Verilog as an additional pipeline stage within a baseline processor. The modified system is simulated at the gate-level in 45 nm CMOS technology, exhibiting a speed-up of 68% and energy reduction of 37% with coarse-grained ML classification. A speed-up of 95% is demonstrated with finer granularities at additional energy costs. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 181,795 |
1803.06746 | Experimental Verification of Rate Flexibility and Probabilistic Shaping
by 4D Signaling | The rate flexibility and probabilistic shaping gain of $4$-dimensional signaling is experimentally tested for short-reach, unrepeated transmission. A rate granularity of 0.5 bits/QAM symbol is achieved with a distribution matcher based on a simple look-up table. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 92,899 |
2208.13078 | MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages | Owing to the lack of corpora for low-resource languages, current works on dialogue generation have mainly focused on English. In this paper, we present mDIA, the first large-scale multilingual benchmark for dialogue generation across low- to high-resource languages. It covers real-life conversations in 46 languages across 19 language families. We present baseline results obtained by fine-tuning the multilingual, non-dialogue-focused pre-trained model mT5 as well as English-centric, dialogue-focused pre-trained chatbot DialoGPT. The results show that mT5-based models perform better on sacreBLEU and BertScore but worse on diversity. Even though promising results are found in few-shot and zero-shot scenarios, there is a large gap between the generation quality in English and other languages. We hope that the release of mDIA could encourage more works on multilingual dialogue generation to promote language diversity. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 314,946 |
2403.05419 | Rethinking Transformers Pre-training for Multi-Spectral Satellite
Imagery | Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets, remote sensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remote sensing imagery or restrict themselves to use only a single type of data modality. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach, named SatMAE++, performs multi-scale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works, the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions, leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5\% for multi-label classification task on BigEarthNet dataset. Our code and pre-trained models are available at \url{https://github.com/techmn/satmae_pp}. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 436,001 |
2403.08203 | Learnable Community-Aware Transformer for Brain Connectome Analysis with
Token Clustering | Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections. These communities play a crucial role in comprehending the functional organization of the brain and its implications for neurological conditions, including Autism Spectrum Disorder (ASD) and biological differences, such as in gender. Traditional models have been constrained by the necessity of predefined community clusters, limiting their flexibility and adaptability in deciphering the brain's functional organization. Furthermore, these models were restricted by a fixed number of communities, hindering their ability to accurately represent the brain's dynamic nature. In this study, we present a token clustering brain transformer-based model ($\texttt{TC-BrainTF}$) for joint community clustering and classification. Our approach proposes a novel token clustering (TC) module based on the transformer architecture, which utilizes learnable prompt tokens with orthogonal loss where each ROI embedding is projected onto the prompt embedding space, effectively clustering ROIs into communities and reducing the dimensions of the node representation via merging with communities. Our results demonstrate that our learnable community-aware model $\texttt{TC-BrainTF}$ offers improved accuracy in identifying ASD and classifying genders through rigorous testing on ABIDE and HCP datasets. Additionally, the qualitative analysis on $\texttt{TC-BrainTF}$ has demonstrated the effectiveness of the designed TC module and its relevance to neuroscience interpretations. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 437,226 |
1703.01657 | Effect of Adaptive and Cooperative Adaptive Cruise Control on Throughput
of Signalized Arterials | The paper evaluates the influence of the maximum vehicle acceleration and variable proportions of ACC/CACC vehicles on the throughput of an intersection. Two cases are studied: (1) free road downstream of the intersection; and (2) red light at some distance downstream of the intersection. Simulation of a 4-mile stretch of an arterial with 13 signalized intersections is used to evaluate the impact of (C)ACC vehicles on the mean and standard deviation of travel time as the proportion of (C)ACC vehicles is increased. The results suggest a very high urban mobility benefit of (C)ACC vehicles at little or no cost in infrastructure. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 69,407 |
2406.11016 | Optimized Speculative Sampling for GPU Hardware Accelerators | In this work, we optimize speculative sampling for parallel hardware accelerators to improve sampling speed. We notice that substantial portions of the intermediate matrices necessary for speculative sampling can be computed concurrently. This allows us to distribute the workload across multiple GPU threads, enabling simultaneous operations on matrix segments within thread blocks. This results in profiling time improvements ranging from 6% to 13% relative to the baseline implementation, without compromising accuracy. To further accelerate speculative sampling, probability distributions parameterized by softmax are approximated by sigmoid. This approximation approach results in significantly greater relative improvements in profiling time, ranging from 37% to 94%, with a minor decline in accuracy. We conduct extensive experiments on both automatic speech recognition and summarization tasks to validate the effectiveness of our optimization methods. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 464,671 |
1407.4430 | Sequential Logistic Principal Component Analysis (SLPCA): Dimensional
Reduction in Streaming Multivariate Binary-State System | Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use profile. Principal Component Analysis (PCA), is the classical way of dimensional reduction. However, traditional Singular Value Decomposition (SVD) based PCA fails to model data which largely deviates from Gaussian distribution. The Bregman Divergence was recently introduced to achieve a generalized PCA framework. If the random variable under dimensional reduction follows Bernoulli distribution, which occurs in many emerging fields, the generalized PCA is called Logistic PCA (LPCA). In this paper, we extend the batch LPCA to a sequential version (i.e. SLPCA), based on the sequential convex optimization theory. The convergence property of this algorithm is discussed compared to the batch version of LPCA (i.e. BLPCA), as well as its performance in reducing the dimension for multivariate binary-state systems. Its application in building energy end-use profile modeling is also investigated. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 34,703 |
2012.02469 | RPT: Relational Pre-trained Transformer Is Almost All You Need towards
Democratizing Data Preparation | Can AI help automate human-easy but computer-hard data preparation tasks that burden data scientists, practitioners, and crowd workers? We answer this question by presenting RPT, a denoising auto-encoder for tuple-to-X models (X could be tuple, token, label, JSON, and so on). RPT is pre-trained for a tuple-to-tuple model by corrupting the input tuple and then learning a model to reconstruct the original tuple. It adopts a Transformer-based neural translation architecture that consists of a bidirectional encoder (similar to BERT) and a left-to-right autoregressive decoder (similar to GPT), leading to a generalization of both BERT and GPT. The pre-trained RPT can already support several common data preparation tasks such as data cleaning, auto-completion and schema matching. Better still, RPT can be fine-tuned on a wide range of data preparation tasks, such as value normalization, data transformation, data annotation, etc. To complement RPT, we also discuss several appealing techniques such as collaborative training and few-shot learning for entity resolution, and few-shot learning and NLP question-answering for information extraction. In addition, we identify a series of research opportunities to advance the field of data preparation. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | 209,780 |
1212.2136 | A class of random fields on complete graphs with tractable partition
function | The aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time. This class includes Ising models with homogeneous pairwise potentials but arbitrary (inhomogeneous) unary potentials. Similarly, the partition function and marginal probabilities can be computed in polynomial time for random fields on complete bipartite graphs, provided they have homogeneous pairwise potentials. We expect that these tractable classes of large scale random fields can be very useful for the evaluation of approximation algorithms by providing exact error estimates. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 20,223 |
2401.00208 | Inpaint4DNeRF: Promptable Spatio-Temporal NeRF Inpainting with
Generative Diffusion Models | Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 418,915 |
1902.00626 | Nonparametric Curve Alignment | Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 120,457 |
2303.08991 | DeltaScore: Fine-Grained Story Evaluation with Perturbations | Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness. In this paper, we introduce DELTASCORE, a novel methodology that employs perturbation techniques for the evaluation of nuanced story aspects. Our central proposition posits that the extent to which a story excels in a specific aspect (e.g., fluency) correlates with the magnitude of its susceptibility to particular perturbations (e.g., the introduction of typos). Given this, we measure the quality of an aspect by calculating the likelihood difference between pre- and post-perturbation states using pre-trained language models. We compare DELTASCORE with existing metrics on storytelling datasets from two domains in five fine-grained story aspects: fluency, coherence, relatedness, logicality, and interestingness. DELTASCORE demonstrates remarkable performance, revealing a surprising finding that a specific perturbation proves highly effective in capturing multiple aspects. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 351,852 |
2312.17216 | SparseProp: Efficient Event-Based Simulation and Training of Sparse
Recurrent Spiking Neural Networks | Spiking Neural Networks (SNNs) are biologically-inspired models that are capable of processing information in streams of action potentials. However, simulating and training SNNs is computationally expensive due to the need to solve large systems of coupled differential equations. In this paper, we introduce SparseProp, a novel event-based algorithm for simulating and training sparse SNNs. Our algorithm reduces the computational cost of both the forward and backward pass operations from O(N) to O(log(N)) per network spike, thereby enabling numerically exact simulations of large spiking networks and their efficient training using backpropagation through time. By leveraging the sparsity of the network, SparseProp eliminates the need to iterate through all neurons at each spike, employing efficient state updates instead. We demonstrate the efficacy of SparseProp across several classical integrate-and-fire neuron models, including a simulation of a sparse SNN with one million LIF neurons. This results in a speed-up exceeding four orders of magnitude relative to previous event-based implementations. Our work provides an efficient and exact solution for training large-scale spiking neural networks and opens up new possibilities for building more sophisticated brain-inspired models. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | false | 418,651 |
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