categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.12710 | null | null | http://arxiv.org/pdf/2404.12710v1 | 2024-04-19T08:36:52Z | 2024-04-19T08:36:52Z | FedMeS: Personalized Federated Continual Learning Leveraging Local
Memory | We focus on the problem of Personalized Federated Continual Learning (PFCL): a group of distributed clients, each with a sequence of local tasks on arbitrary data distributions, collaborate through a central server to train a personalized model at each client, with the model expected to achieve good performance on all local tasks. We propose a novel PFCL framework called Federated Memory Strengthening FedMeS to address the challenges of client drift and catastrophic forgetting. In FedMeS, each client stores samples from previous tasks using a small amount of local memory, and leverages this information to both 1) calibrate gradient updates in training process; and 2) perform KNN-based Gaussian inference to facilitate personalization. FedMeS is designed to be task-oblivious, such that the same inference process is applied to samples from all tasks to achieve good performance. FedMeS is analyzed theoretically and evaluated experimentally. It is shown to outperform all baselines in average accuracy and forgetting rate, over various combinations of datasets, task distributions, and client numbers. | [
"['Jin Xie' 'Chenqing Zhu' 'Songze Li']"
]
|
null | null | 2404.12711 | null | null | http://arxiv.org/pdf/2404.12711v1 | 2024-04-19T08:40:52Z | 2024-04-19T08:40:52Z | Dynamic Temperature Knowledge Distillation | Temperature plays a pivotal role in moderating label softness in the realm of knowledge distillation (KD). Traditional approaches often employ a static temperature throughout the KD process, which fails to address the nuanced complexities of samples with varying levels of difficulty and overlooks the distinct capabilities of different teacher-student pairings. This leads to a less-than-ideal transfer of knowledge. To improve the process of knowledge propagation, we proposed Dynamic Temperature Knowledge Distillation (DTKD) which introduces a dynamic, cooperative temperature control for both teacher and student models simultaneously within each training iterafion. In particular, we proposed "textbf{sharpness}" as a metric to quantify the smoothness of a model's output distribution. By minimizing the sharpness difference between the teacher and the student, we can derive sample-specific temperatures for them respectively. Extensive experiments on CIFAR-100 and ImageNet-2012 demonstrate that DTKD performs comparably to leading KD techniques, with added robustness in Target Class KD and None-target Class KD scenarios.The code is available at https://github.com/JinYu1998/DTKD. | [
"['Yukang Wei' 'Yu Bai']"
]
|
null | null | 2404.12712 | null | null | http://arxiv.org/pdf/2404.12712v1 | 2024-04-19T08:46:33Z | 2024-04-19T08:46:33Z | uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories | Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper addresses two issues: the lack of labeled data and the difficulty of explaining the predictions of a neural network. To this end, we present a framework called uTRAND, that shifts the problem of anomalous trajectory prediction from the pixel space to a semantic-topological domain. The framework detects and tracks all types of traffic agents in bird's-eye-view videos of traffic cameras mounted at an intersection. By conceptualizing the intersection as a patch-based graph, it is shown that the framework learns and models the normal behaviour of traffic agents without costly manual labeling. Furthermore, uTRAND allows to formulate simple rules to classify anomalous trajectories in a way suited for human interpretation. We show that uTRAND outperforms other state-of-the-art approaches on a dataset of anomalous trajectories collected in a real-world setting, while producing explainable detection results. | [
"[\"Giacomo D'Amicantonio\" 'Egor Bondarau' 'Peter H. N. de With']"
]
|
null | null | 2404.12718 | null | null | http://arxiv.org/pdf/2404.12718v2 | 2024-07-09T08:33:59Z | 2024-04-19T08:58:53Z | Improving Prediction Accuracy of Semantic Segmentation Methods Using
Convolutional Autoencoder Based Pre-processing Layers | In this paper, we propose a method to improve prediction accuracy of semantic segmentation methods as follows: (1) construct a neural network that has pre-processing layers based on a convolutional autoencoder ahead of a semantic segmentation network, and (2) train the entire network initialized by the weights of the pre-trained autoencoder. We applied this method to the fully convolutional network (FCN) and experimentally compared its prediction accuracy on the cityscapes dataset. The Mean IoU of the proposed target model with the He normal initialization is 18.7% higher than that of FCN with the He normal initialization. In addition, those of the modified models of the target model are significantly higher than that of FCN with the He normal initialization. The accuracy and loss curves during the training showed that these are resulting from the improvement of the generalization ability. All of these results provide strong evidence that the proposed method is significantly effective in improving the prediction accuracy of FCN. The proposed method has the following features: it is comparatively simple, whereas the effect on improving the generalization ability and prediction accuracy of FCN is significant; the increase in the number of parameters by using it is very small, and that in the computation time is substantially large. In principle, the proposed method can be applied to other semantic segmentation methods. For semantic segmentation, at present, there is no effective way to improve the prediction accuracy of existing methods. None have published a method which is the same as or similar to our method and none have used such a method in practice. Therefore, we believe that our method is useful in practice and worthy of being widely known and used. | [
"['Hisashi Shimodaira']"
]
|
null | null | 2404.12721 | null | null | http://arxiv.org/pdf/2404.12721v1 | 2024-04-19T09:01:58Z | 2024-04-19T09:01:58Z | Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in
Land Cover Mapping via Hybrid Semantic Segmentation Framework | Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a generalized few-shot segmentation-based framework, named SegLand, to update novel classes in high-resolution land-cover mapping. Specifically, the proposed framework is designed in three parts: (a) Data pre-processing: the base training set and the few-shot support sets of novel classes are analyzed and augmented; (b) Hybrid segmentation structure; Multiple base learners and a modified Projection onto Orthogonal Prototypes (POP) network are combined to enhance the base-class recognition and to dig novel classes from insufficient labels data; (c) Ultimate fusion: the semantic segmentation results of the base learners and POP network are reasonably fused. The proposed framework has won first place in the leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge. Experiments demonstrate the superiority of the framework for automatically updating novel land-cover classes with limited labeled data. | [
"['Zhuohong Li' 'Fangxiao Lu' 'Jiaqi Zou' 'Lei Hu' 'Hongyan Zhang']"
]
|
null | null | 2404.12724 | null | null | http://arxiv.org/pdf/2404.12724v2 | 2024-04-25T06:04:17Z | 2024-04-19T09:08:12Z | Graph Convolutional Network For Semi-supervised Node Classification With
Subgraph Sketching | In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning layer. We apply GLDGCN to the semi-supervised node classification task. Compared with the baseline methods, we achieve higher classification accuracy on three citation networks Citeseer, Cora and Pubmed, and we also analyze and discussabout selection of the hyperparameters and network depth. GLDGCN also perform well on the classic social network KarateClub and the new Wiki-CS dataset. For the insufficient ability of our algorithm to process large graphs during the experiment, we also introduce subgraph clustering and stochastic gradient descent methods into GCN and design a semi-supervised node classification algorithm based on the CLustering Graph Convolutional neural Network, which enables GCN to process large graph and improves its application value. We complete semi-supervised node classification experiments on two classic large graph which are PPI dataset (more than 50,000 nodes) and Reddit dataset (more than 200,000 nodes), and also perform well. | [
"['Zibin Huang' 'Jun Xian']"
]
|
null | null | 2404.12725 | null | null | http://arxiv.org/pdf/2404.12725v2 | 2024-05-05T08:00:17Z | 2024-04-19T09:08:44Z | Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual
Target Speech Extraction | The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. Our approach partitions the audio-visual target speech extraction task into two stages: speech perception and speech production. In the speech perception stage, audio serves as the dominant modality, while visual information acts as the conditional modality. Conversely, in the speech production stage, the roles are reversed. This transformation of modality status aims to alleviate the problem of modality imbalance. Additionally, we introduce a contrastive semantic matching loss to ensure that the semantic information conveyed by the generated speech aligns with the semantic information conveyed by lip movements during the speech production stage. Through extensive experiments conducted on multiple benchmark datasets for audio-visual target speech extraction, we showcase the superior performance achieved by our proposed method. | [
"['Zhaoxi Mu' 'Xinyu Yang']"
]
|
null | null | 2404.12730 | null | null | http://arxiv.org/pdf/2404.12730v1 | 2024-04-19T09:22:20Z | 2024-04-19T09:22:20Z | PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian
Differential Privacy | Conditional Generative Adversarial Networks (CGANs) exhibit significant potential in supervised learning model training by virtue of their ability to generate realistic labeled images. However, numerous studies have indicated the privacy leakage risk in CGANs models. The solution DPCGAN, incorporating the differential privacy framework, faces challenges such as heavy reliance on labeled data for model training and potential disruptions to original gradient information due to excessive gradient clipping, making it difficult to ensure model accuracy. To address these challenges, we present a privacy-preserving training framework called PATE-TripleGAN. This framework incorporates a classifier to pre-classify unlabeled data, establishing a three-party min-max game to reduce dependence on labeled data. Furthermore, we present a hybrid gradient desensitization algorithm based on the Private Aggregation of Teacher Ensembles (PATE) framework and Differential Private Stochastic Gradient Descent (DPSGD) method. This algorithm allows the model to retain gradient information more effectively while ensuring privacy protection, thereby enhancing the model's utility. Privacy analysis and extensive experiments affirm that the PATE-TripleGAN model can generate a higher quality labeled image dataset while ensuring the privacy of the training data. | [
"['Zepeng Jiang' 'Weiwei Ni' 'Yifan Zhang']"
]
|
null | null | 2404.12741 | null | null | http://arxiv.org/pdf/2404.12741v1 | 2024-04-19T09:36:48Z | 2024-04-19T09:36:48Z | Multi-Class Quantum Convolutional Neural Networks | Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy loss through parameterized quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes. The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network. | [
"['Marco Mordacci' 'Davide Ferrari' 'Michele Amoretti']"
]
|
null | null | 2404.12745 | null | null | http://arxiv.org/pdf/2404.12745v1 | 2024-04-19T09:46:45Z | 2024-04-19T09:46:45Z | Recurrent Neural Networks for Modelling Gross Primary Production | Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO$_2$ flux, especially significant for forests. Eddy Covariance (EC) measurements are widely used for ecosystem-scale GPP quantification but are globally sparse. In areas lacking local EC measurements, remote sensing (RS) data are typically utilised to estimate GPP after statistically relating them to in-situ data. Deep learning offers novel perspectives, and the potential of recurrent neural network architectures for estimating daily GPP remains underexplored. This study presents a comparative analysis of three architectures: Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long-Short Term Memory (LSTMs). Our findings reveal comparable performance across all models for full-year and growing season predictions. Notably, LSTMs outperform in predicting climate-induced GPP extremes. Furthermore, our analysis highlights the importance of incorporating radiation and RS inputs (optical, temperature, and radar) for accurate GPP predictions, particularly during climate extremes. | [
"['David Montero' 'Miguel D. Mahecha' 'Francesco Martinuzzi' 'César Aybar'\n 'Anne Klosterhalfen' 'Alexander Knohl' 'Franziska Koebsch' 'Jesús Anaya'\n 'Sebastian Wieneke']"
]
|
null | null | 2404.12754 | null | null | http://arxiv.org/pdf/2404.12754v1 | 2024-04-19T10:00:34Z | 2024-04-19T10:00:34Z | Adaptive Regularization of Representation Rank as an Implicit Constraint
of Bellman Equation | Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of value networks. Existing studies focus on unboundedly maximizing this rank; nevertheless, that approach would introduce overly complex models in the learning, thus undermining performance. Hence, fine-tuning representation rank presents a challenging and crucial optimization problem. To address this issue, we find a guiding principle for adaptive control of the representation rank. We employ the Bellman equation as a theoretical foundation and derive an upper bound on the cosine similarity of consecutive state-action pairs representations of value networks. We then leverage this upper bound to propose a novel regularizer, namely BEllman Equation-based automatic rank Regularizer (BEER). This regularizer adaptively regularizes the representation rank, thus improving the DRL agent's performance. We first validate the effectiveness of automatic control of rank on illustrative experiments. Then, we scale up BEER to complex continuous control tasks by combining it with the deterministic policy gradient method. Among 12 challenging DeepMind control tasks, BEER outperforms the baselines by a large margin. Besides, BEER demonstrates significant advantages in Q-value approximation. Our code is available at https://github.com/sweetice/BEER-ICLR2024. | [
"['Qiang He' 'Tianyi Zhou' 'Meng Fang' 'Setareh Maghsudi']"
]
|
null | null | 2404.12759 | null | null | http://arxiv.org/pdf/2404.12759v1 | 2024-04-19T10:02:53Z | 2024-04-19T10:02:53Z | decoupleQ: Towards 2-bit Post-Training Uniform Quantization via
decoupling Parameters into Integer and Floating Points | Quantization emerges as one of the most promising compression technologies for deploying efficient large models for various real time application in recent years. Considering that the storage and IO of weights take up the vast majority of the overhead inside a large model, weight only quantization can lead to large gains. However, existing quantization schemes suffer from significant accuracy degradation at very low bits, or require some additional computational overhead when deployed, making it difficult to be applied to large-scale applications in industry. In this paper, we propose decoupleQ, achieving a substantial increase in model accuracy, especially at very low bits. decoupleQ abandons the traditional heuristic quantization paradigm and decouples the model parameters into integer and floating-point parts, thus transforming the quantization problem into a traditional mathematical optimization problem with constraints, which is then solved alternatively by off-the-shelf optimization methods. Quantization via decoupleQ is linear and uniform, making it hardware-friendlier than non-uniform counterpart, and enabling the idea to be migrated to high-bit quantization to enhance its robustness. Our method has achieved well on-line accuracy near fp16/bf16 on the 2-bit quantization of large speech models in ByteDance. The code is available at https://github.com/bytedance/decoupleQ | [
"['Yi Guo' 'Fanliu Kong' 'Xiaoyang Li' 'Hui Li' 'Wei Chen' 'Xiaogang Tian'\n 'Jinping Cai' 'Yang Zhang' 'Shouda Liu']"
]
|
null | null | 2404.12766 | null | null | http://arxiv.org/pdf/2404.12766v2 | 2024-06-08T16:36:17Z | 2024-04-19T10:10:39Z | Continual Learning on a Diet: Learning from Sparsely Labeled Streams
Under Constrained Computation | We propose and study a realistic Continual Learning (CL) setting where learning algorithms are granted a restricted computational budget per time step while training. We apply this setting to large-scale semi-supervised Continual Learning scenarios with sparse label rates. Previous proficient CL methods perform very poorly in this challenging setting. Overfitting to the sparse labeled data and insufficient computational budget are the two main culprits for such a poor performance. Our new setting encourages learning methods to effectively and efficiently utilize the unlabeled data during training. To that end, we propose a simple but highly effective baseline, DietCL, which utilizes both unlabeled and labeled data jointly. DietCL meticulously allocates computational budget for both types of data. We validate our baseline, at scale, on several datasets, e.g., CLOC, ImageNet10K, and CGLM, under constraint budget setups. DietCL outperforms, by a large margin, all existing supervised CL algorithms as well as more recent continual semi-supervised methods. Our extensive analysis and ablations demonstrate that DietCL is stable under a full spectrum of label sparsity, computational budget, and various other ablations. | [
"['Wenxuan Zhang' 'Youssef Mohamed' 'Bernard Ghanem' 'Philip H. S. Torr'\n 'Adel Bibi' 'Mohamed Elhoseiny']"
]
|
null | null | 2404.12770 | null | null | http://arxiv.org/pdf/2404.12770v1 | 2024-04-19T10:21:33Z | 2024-04-19T10:21:33Z | Camera Agnostic Two-Head Network for Ego-Lane Inference | Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems. The traditional approach necessitates well-calibrated cameras, which confines variation of camera configuration, as the algorithm relies on intrinsic and extrinsic calibration. In this paper, we propose a learning-based ego-lane inference by directly estimating the ego-lane index from a single image. To enhance robust performance, our model incorporates the two-head structure inferring ego-lane in two perspectives simultaneously. Furthermore, we utilize an attention mechanism guided by vanishing point-and-line to adapt to changes in viewpoint without requiring accurate calibration. The high adaptability of our model was validated in diverse environments, devices, and camera mounting points and orientations. | [
"['Chaehyeon Song' 'Sungho Yoon' 'Minhyeok Heo' 'Ayoung Kim' 'Sujung Kim']"
]
|
null | null | 2404.12784 | null | null | http://arxiv.org/pdf/2404.12784v1 | 2024-04-19T10:47:53Z | 2024-04-19T10:47:53Z | Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation | We introduce Contrastive Gaussian Clustering, a novel approach capable of provide segmentation masks from any viewpoint and of enabling 3D segmentation of the scene. Recent works in novel-view synthesis have shown how to model the appearance of a scene via a cloud of 3D Gaussians, and how to generate accurate images from a given viewpoint by projecting on it the Gaussians before $alpha$ blending their color. Following this example, we train a model to include also a segmentation feature vector for each Gaussian. These can then be used for 3D scene segmentation, by clustering Gaussians according to their feature vectors; and to generate 2D segmentation masks, by projecting the Gaussians on a plane and $alpha$ blending over their segmentation features. Using a combination of contrastive learning and spatial regularization, our method can be trained on inconsistent 2D segmentation masks, and still learn to generate segmentation masks consistent across all views. Moreover, the resulting model is extremely accurate, improving the IoU accuracy of the predicted masks by $+8%$ over the state of the art. Code and trained models will be released soon. | [
"['Myrna C. Silva' 'Mahtab Dahaghin' 'Matteo Toso' 'Alessio Del Bue']"
]
|
null | null | 2404.12792 | null | null | http://arxiv.org/pdf/2404.12792v1 | 2024-04-19T11:09:55Z | 2024-04-19T11:09:55Z | Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using
Deep Learning | Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure. Yet, in learning large-scale data challenges arise, such as the curse of dimensionality and training complexity of FLSs. The complexity is due mainly to the constraints to be satisfied as the learnable parameters define FSs and the complexity of the center of the sets calculation method, especially of IT2-FLSs. This paper explicitly focuses on the learning problem of FLSs and presents a computationally efficient learning method embedded within the realm of Deep Learning (DL). The proposed method tackles the learning challenges of FLSs by presenting computationally efficient implementations of FLSs, thereby minimizing training time while leveraging mini-batched DL optimizers and automatic differentiation provided within the DL frameworks. We illustrate the efficiency of the DL framework for FLSs on benchmark datasets. | [
"['Ata Koklu' 'Yusuf Guven' 'Tufan Kumbasar']"
]
|
null | null | 2404.12800 | null | null | http://arxiv.org/pdf/2404.12800v1 | 2024-04-19T11:29:10Z | 2024-04-19T11:29:10Z | Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality
Prediction Intervals | General Type-2 (GT2) Fuzzy Logic Systems (FLSs) are perfect candidates to quantify uncertainty, which is crucial for informed decisions in high-risk tasks, as they are powerful tools in representing uncertainty. In this paper, we travel back in time to provide a new look at GT2-FLSs by adopting Zadeh's (Z) GT2 Fuzzy Set (FS) definition, intending to learn GT2-FLSs that are capable of achieving reliable High-Quality Prediction Intervals (HQ-PI) alongside precision. By integrating Z-GT2-FS with the (alpha)-plane representation, we show that the design flexibility of GT2-FLS is increased as it takes away the dependency of the secondary membership function from the primary membership function. After detailing the construction of Z-GT2-FLSs, we provide solutions to challenges while learning from high-dimensional data: the curse of dimensionality, and integrating Deep Learning (DL) optimizers. We develop a DL framework for learning dual-focused Z-GT2-FLSs with high performances. Our study includes statistical analyses, highlighting that the Z-GT2-FLS not only exhibits high-precision performance but also produces HQ-PIs in comparison to its GT2 and IT2 fuzzy counterparts which have more learnable parameters. The results show that the Z-GT2-FLS has a huge potential in uncertainty quantification. | [
"['Yusuf Guven' 'Ata Koklu' 'Tufan Kumbasar']"
]
|
null | null | 2404.12802 | null | null | http://arxiv.org/pdf/2404.12802v1 | 2024-04-19T11:37:51Z | 2024-04-19T11:37:51Z | Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision
and Prediction Intervals | In this paper, we tackle the task of generating Prediction Intervals (PIs) in high-risk scenarios by proposing enhancements for learning Interval Type-2 (IT2) Fuzzy Logic Systems (FLSs) to address their learning challenges. In this context, we first provide extra design flexibility to the Karnik-Mendel (KM) and Nie-Tan (NT) center of sets calculation methods to increase their flexibility for generating PIs. These enhancements increase the flexibility of KM in the defuzzification stage while the NT in the fuzzification stage. To address the large-scale learning challenge, we transform the IT2-FLS's constraint learning problem into an unconstrained form via parameterization tricks, enabling the direct application of deep learning optimizers. To address the curse of dimensionality issue, we expand the High-Dimensional Takagi-Sugeno-Kang (HTSK) method proposed for type-1 FLS to IT2-FLSs, resulting in the HTSK2 approach. Additionally, we introduce a framework to learn the enhanced IT2-FLS with a dual focus, aiming for high precision and PI generation. Through exhaustive statistical results, we reveal that HTSK2 effectively addresses the dimensionality challenge, while the enhanced KM and NT methods improved learning and enhanced uncertainty quantification performances of IT2-FLSs. | [
"['Ata Koklu' 'Yusuf Guven' 'Tufan Kumbasar']"
]
|
null | null | 2404.12803 | null | null | http://arxiv.org/pdf/2404.12803v1 | 2024-04-19T11:38:08Z | 2024-04-19T11:38:08Z | TextSquare: Scaling up Text-Centric Visual Instruction Tuning | Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data. To this end, we introduce a new approach for creating a massive, high-quality instruction-tuning dataset, Square-10M, which is generated using closed-source MLLMs. The data construction process, termed Square, consists of four steps: Self-Questioning, Answering, Reasoning, and Evaluation. Our experiments with Square-10M led to three key findings: 1) Our model, TextSquare, considerably surpasses open-source previous state-of-the-art Text-centric MLLMs and sets a new standard on OCRBench(62.2%). It even outperforms top-tier models like GPT4V and Gemini in 6 of 10 text-centric benchmarks. 2) Additionally, we demonstrate the critical role of VQA reasoning data in offering comprehensive contextual insights for specific questions. This not only improves accuracy but also significantly mitigates hallucinations. Specifically, TextSquare scores an average of 75.1% across four general VQA and hallucination evaluation datasets, outperforming previous state-of-the-art models. 3) Notably, the phenomenon observed in scaling text-centric VQA datasets reveals a vivid pattern: the exponential increase of instruction tuning data volume is directly proportional to the improvement in model performance, thereby validating the necessity of the dataset scale and the high quality of Square-10M. | [
"['Jingqun Tang' 'Chunhui Lin' 'Zhen Zhao' 'Shu Wei' 'Binghong Wu' 'Qi Liu'\n 'Hao Feng' 'Yang Li' 'Siqi Wang' 'Lei Liao' 'Wei Shi' 'Yuliang Liu'\n 'Hao Liu' 'Yuan Xie' 'Xiang Bai' 'Can Huang']"
]
|
null | null | 2404.12810 | null | null | http://arxiv.org/pdf/2404.12810v1 | 2024-04-19T11:47:17Z | 2024-04-19T11:47:17Z | Enhancing Counterfactual Explanation Search with Diffusion Distance and
Directional Coherence | A pressing issue in the adoption of AI models is the increasing demand for more human-centric explanations of their predictions. To advance towards more human-centric explanations, understanding how humans produce and select explanations has been beneficial. In this work, inspired by insights of human cognition we propose and test the incorporation of two novel biases to enhance the search for effective counterfactual explanations. Central to our methodology is the application of diffusion distance, which emphasizes data connectivity and actionability in the search for feasible counterfactual explanations. In particular, diffusion distance effectively weights more those points that are more interconnected by numerous short-length paths. This approach brings closely connected points nearer to each other, identifying a feasible path between them. We also introduce a directional coherence term that allows the expression of a preference for the alignment between the joint and marginal directional changes in feature space to reach a counterfactual. This term enables the generation of counterfactual explanations that align with a set of marginal predictions based on expectations of how the outcome of the model varies by changing one feature at a time. We evaluate our method, named Coherent Directional Counterfactual Explainer (CoDiCE), and the impact of the two novel biases against existing methods such as DiCE, FACE, Prototypes, and Growing Spheres. Through a series of ablation experiments on both synthetic and real datasets with continuous and mixed-type features, we demonstrate the effectiveness of our method. | [
"['Marharyta Domnich' 'Raul Vicente']"
]
|
null | null | 2404.12814 | null | null | http://arxiv.org/pdf/2404.12814v2 | 2024-04-22T01:14:11Z | 2024-04-19T11:49:01Z | Generative Modelling with High-Order Langevin Dynamics | Diffusion generative modelling (DGM) based on stochastic differential equations (SDEs) with score matching has achieved unprecedented results in data generation. In this paper, we propose a novel fast high-quality generative modelling method based on high-order Langevin dynamics (HOLD) with score matching. This motive is proved by third-order Langevin dynamics. By augmenting the previous SDEs, e.g. variance exploding or variance preserving SDEs for single-data variable processes, HOLD can simultaneously model position, velocity, and acceleration, thereby improving the quality and speed of the data generation at the same time. HOLD is composed of one Ornstein-Uhlenbeck process and two Hamiltonians, which reduce the mixing time by two orders of magnitude. Empirical experiments for unconditional image generation on the public data set CIFAR-10 and CelebA-HQ show that the effect is significant in both Frechet inception distance (FID) and negative log-likelihood, and achieves the state-of-the-art FID of 1.85 on CIFAR-10. | [
"['Ziqiang Shi' 'Rujie Liu']"
]
|
null | null | 2404.12824 | null | null | http://arxiv.org/pdf/2404.12824v1 | 2024-04-19T12:00:10Z | 2024-04-19T12:00:10Z | MAexp: A Generic Platform for RL-based Multi-Agent Exploration | The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the inefficiency in sampling and the lack of diversity in Multi-Agent Reinforcement Learning (MARL) algorithms across different scenarios, restraining their widespread applications. To fill these gaps, we propose MAexp, a generic platform for multi-agent exploration that integrates a broad range of state-of-the-art MARL algorithms and representative scenarios. Moreover, we employ point clouds to represent our exploration scenarios, leading to high-fidelity environment mapping and a sampling speed approximately 40 times faster than existing platforms. Furthermore, equipped with an attention-based Multi-Agent Target Generator and a Single-Agent Motion Planner, MAexp can work with arbitrary numbers of agents and accommodate various types of robots. Extensive experiments are conducted to establish the first benchmark featuring several high-performance MARL algorithms across typical scenarios for robots with continuous actions, which highlights the distinct strengths of each algorithm in different scenarios. | [
"['Shaohao Zhu' 'Jiacheng Zhou' 'Anjun Chen' 'Mingming Bai' 'Jiming Chen'\n 'Jinming Xu']"
]
|
null | null | 2404.12832 | null | null | http://arxiv.org/pdf/2404.12832v1 | 2024-04-19T12:09:49Z | 2024-04-19T12:09:49Z | COIN: Counterfactual inpainting for weakly supervised semantic
segmentation for medical images | Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models, particularly in segmentation tasks, is often limited by the need for extensive annotated datasets. To address this challenge, the capabilities of weakly supervised semantic segmentation are explored through the lens of Explainable AI and the generation of counterfactual explanations. The scope of this research is development of a novel counterfactual inpainting approach (COIN) that flips the predicted classification label from abnormal to normal by using a generative model. For instance, if the classifier deems an input medical image X as abnormal, indicating the presence of a pathology, the generative model aims to inpaint the abnormal region, thus reversing the classifier's original prediction label. The approach enables us to produce precise segmentations for pathologies without depending on pre-existing segmentation masks. Crucially, image-level labels are utilized, which are substantially easier to acquire than creating detailed segmentation masks. The effectiveness of the method is demonstrated by segmenting synthetic targets and actual kidney tumors from CT images acquired from Tartu University Hospital in Estonia. The findings indicate that COIN greatly surpasses established attribution methods, such as RISE, ScoreCAM, and LayerCAM, as well as an alternative counterfactual explanation method introduced by Singla et al. This evidence suggests that COIN is a promising approach for semantic segmentation of tumors in CT images, and presents a step forward in making deep learning applications more accessible and effective in healthcare, where annotated data is scarce. | [
"['Dmytro Shvetsov' 'Joonas Ariva' 'Marharyta Domnich' 'Raul Vicente'\n 'Dmytro Fishman']"
]
|
null | null | 2404.12839 | null | null | http://arxiv.org/pdf/2404.12839v1 | 2024-04-19T12:20:49Z | 2024-04-19T12:20:49Z | ECOR: Explainable CLIP for Object Recognition | Large Vision Language Models (VLMs), such as CLIP, have significantly contributed to various computer vision tasks, including object recognition and object detection. Their open vocabulary feature enhances their value. However, their black-box nature and lack of explainability in predictions make them less trustworthy in critical domains. Recently, some work has been done to force VLMs to provide reasonable rationales for object recognition, but this often comes at the expense of classification accuracy. In this paper, we first propose a mathematical definition of explainability in the object recognition task based on the joint probability distribution of categories and rationales, then leverage this definition to fine-tune CLIP in an explainable manner. Through evaluations of different datasets, our method demonstrates state-of-the-art performance in explainable classification. Notably, it excels in zero-shot settings, showcasing its adaptability. This advancement improves explainable object recognition, enhancing trust across diverse applications. The code will be made available online upon publication. | [
"['Ali Rasekh' 'Sepehr Kazemi Ranjbar' 'Milad Heidari' 'Wolfgang Nejdl']"
]
|
null | null | 2404.12841 | null | null | http://arxiv.org/pdf/2404.12841v1 | 2024-04-19T12:21:27Z | 2024-04-19T12:21:27Z | Explainable Deepfake Video Detection using Convolutional Neural Network
and CapsuleNet | Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios. | [
"['Gazi Hasin Ishrak' 'Zalish Mahmud' 'MD. Zami Al Zunaed Farabe'\n 'Tahera Khanom Tinni' 'Tanzim Reza' 'Mohammad Zavid Parvez']"
]
|
null | null | 2404.12843 | null | null | http://arxiv.org/pdf/2404.12843v1 | 2024-04-19T12:23:57Z | 2024-04-19T12:23:57Z | Towards Logically Consistent Language Models via Probabilistic Reasoning | Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning to external tools. In this work, we strive for a middle ground and introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules. Fine-tuning with our loss on a limited set of facts enables our LLMs to be more logically consistent than previous baselines and allows them to extrapolate to unseen but semantically similar factual knowledge more systematically. | [
"['Diego Calanzone' 'Stefano Teso' 'Antonio Vergari']"
]
|
null | null | 2404.12846 | null | null | http://arxiv.org/pdf/2404.12846v1 | 2024-04-19T12:26:45Z | 2024-04-19T12:26:45Z | KoReA-SFL: Knowledge Replay-based Split Federated Learning Against
Catastrophic Forgetting | Although Split Federated Learning (SFL) is good at enabling knowledge sharing among resource-constrained clients, it suffers from the problem of low training accuracy due to the neglect of data heterogeneity and catastrophic forgetting. To address this issue, we propose a novel SFL approach named KoReA-SFL, which adopts a multi-model aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and a knowledge replay strategy to deal with catastrophic forgetting. Specifically, in KoReA-SFL cloud servers (i.e., fed server and main server) maintain multiple branch model portions rather than a global portion for local training and an aggregated master-model portion for knowledge sharing among branch portions. To avoid catastrophic forgetting, the main server of KoReA-SFL selects multiple assistant devices for knowledge replay according to the training data distribution of each server-side branch-model portion. Experimental results obtained from non-IID and IID scenarios demonstrate that KoReA-SFL significantly outperforms conventional SFL methods (by up to 23.25% test accuracy improvement). | [
"['Zeke Xia' 'Ming Hu' 'Dengke Yan' 'Ruixuan Liu' 'Anran Li' 'Xiaofei Xie'\n 'Mingsong Chen']"
]
|
null | null | 2404.12850 | null | null | http://arxiv.org/pdf/2404.12850v1 | 2024-04-19T12:39:11Z | 2024-04-19T12:39:11Z | CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and
Feature Balance | Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massive AIoT devices, respectively. To address the above challenges, we present a novel asynchronous FL approach named CaBaFL, which includes a hierarchical Cache-based aggregation mechanism and a feature Balance-guided device selection strategy. CaBaFL maintains multiple intermediate models simultaneously for local training. The hierarchical cache-based aggregation mechanism enables each intermediate model to be trained on multiple devices to align the training time and mitigate the straggler issue. In specific, each intermediate model is stored in a low-level cache for local training and when it is trained by sufficient local devices, it will be stored in a high-level cache for aggregation. To address the problem of imbalanced data, the feature balance-guided device selection strategy in CaBaFL adopts the activation distribution as a metric, which enables each intermediate model to be trained across devices with totally balanced data distributions before aggregation. Experimental results show that compared with the state-of-the-art FL methods, CaBaFL achieves up to 9.26X training acceleration and 19.71% accuracy improvements. | [
"['Zeke Xia' 'Ming Hu' 'Dengke Yan' 'Xiaofei Xie' 'Tianlin Li' 'Anran Li'\n 'Junlong Zhou' 'Mingsong Chen']"
]
|
null | null | 2404.12852 | null | null | http://arxiv.org/pdf/2404.12852v1 | 2024-04-19T12:42:31Z | 2024-04-19T12:42:31Z | LSP Framework: A Compensatory Model for Defeating Trigger Reverse
Engineering via Label Smoothing Poisoning | Deep neural networks are vulnerable to backdoor attacks. Among the existing backdoor defense methods, trigger reverse engineering based approaches, which reconstruct the backdoor triggers via optimizations, are the most versatile and effective ones compared to other types of methods. In this paper, we summarize and construct a generic paradigm for the typical trigger reverse engineering process. Based on this paradigm, we propose a new perspective to defeat trigger reverse engineering by manipulating the classification confidence of backdoor samples. To determine the specific modifications of classification confidence, we propose a compensatory model to compute the lower bound of the modification. With proper modifications, the backdoor attack can easily bypass the trigger reverse engineering based methods. To achieve this objective, we propose a Label Smoothing Poisoning (LSP) framework, which leverages label smoothing to specifically manipulate the classification confidences of backdoor samples. Extensive experiments demonstrate that the proposed work can defeat the state-of-the-art trigger reverse engineering based methods, and possess good compatibility with a variety of existing backdoor attacks. | [
"['Beichen Li' 'Yuanfang Guo' 'Heqi Peng' 'Yangxi Li' 'Yunhong Wang']"
]
|
null | null | 2404.12856 | null | null | http://arxiv.org/pdf/2404.12856v2 | 2024-06-18T07:34:33Z | 2024-04-19T12:50:43Z | Language-Driven Active Learning for Diverse Open-Set 3D Object Detection | Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection. Our method leverages active learning techniques to query diverse and informative data samples from an unlabeled pool, enhancing the model's ability to detect underrepresented or novel objects. Specifically, we introduce the Vision-Language Embedding Diversity Querying (VisLED-Querying) algorithm, which operates in both open-world exploring and closed-world mining settings. In open-world exploring, VisLED-Querying selects data points most novel relative to existing data, while in closed-world mining, it mines novel instances of known classes. We evaluate our approach on the nuScenes dataset and demonstrate its efficiency compared to random sampling and entropy-querying methods. Our results show that VisLED-Querying consistently outperforms random sampling and offers competitive performance compared to entropy-querying despite the latter's model-optimality, highlighting the potential of VisLED for improving object detection in autonomous driving scenarios. We make our code publicly available at https://github.com/Bjork-crypto/VisLED-Querying | [
"['Ross Greer' 'Bjørk Antoniussen' 'Andreas Møgelmose' 'Mohan Trivedi']"
]
|
null | null | 2404.12862 | null | null | http://arxiv.org/pdf/2404.12862v1 | 2024-04-19T13:01:59Z | 2024-04-19T13:01:59Z | A Guide to Feature Importance Methods for Scientific Inference | While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide, due to their opaque internal mechanisms. Feature importance (FI) methods provide useful insights into the DGP under certain conditions. Since the results of different FI methods have different interpretations, selecting the correct FI method for a concrete use case is crucial and still requires expert knowledge. This paper serves as a comprehensive guide to help understand the different interpretations of FI methods. Through an extensive review of FI methods and providing new proofs regarding their interpretation, we facilitate a thorough understanding of these methods and formulate concrete recommendations for scientific inference. We conclude by discussing options for FI uncertainty estimation and point to directions for future research aiming at full statistical inference from black-box ML models. | [
"['Fiona Katharina Ewald' 'Ludwig Bothmann' 'Marvin N. Wright'\n 'Bernd Bischl' 'Giuseppe Casalicchio' 'Gunnar König']"
]
|
null | null | 2404.12876 | null | null | http://arxiv.org/pdf/2404.12876v1 | 2024-04-19T13:25:27Z | 2024-04-19T13:25:27Z | A Large-scale Medical Visual Task Adaptation Benchmark | Visual task adaptation has been demonstrated to be effective in adapting pre-trained Vision Transformers (ViTs) to general downstream visual tasks using specialized learnable layers or tokens. However, there is yet a large-scale benchmark to fully explore the effect of visual task adaptation on the realistic and important medical domain, particularly across diverse medical visual modalities, such as color images, X-ray, and CT. To close this gap, we present Med-VTAB, a large-scale Medical Visual Task Adaptation Benchmark consisting of 1.68 million medical images for diverse organs, modalities, and adaptation approaches. Based on Med-VTAB, we explore the scaling law of medical prompt tuning concerning tunable parameters and the generalizability of medical visual adaptation using non-medical/medical pre-train weights. Besides, we study the impact of patient ID out-of-distribution on medical visual adaptation, which is a real and challenging scenario. Furthermore, results from Med-VTAB indicate that a single pre-trained model falls short in medical task adaptation. Therefore, we introduce GMoE-Adapter, a novel method that combines medical and general pre-training weights through a gated mixture-of-experts adapter, achieving state-of-the-art results in medical visual task adaptation. | [
"['Shentong Mo' 'Xufang Luo' 'Yansen Wang' 'Dongsheng Li']"
]
|
null | null | 2404.12886 | null | null | http://arxiv.org/pdf/2404.12886v1 | 2024-04-19T13:40:25Z | 2024-04-19T13:40:25Z | MCM: Multi-condition Motion Synthesis Framework | Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions. Text and audio represent the two predominant modalities employed as HMS control conditions. While existing research has primarily focused on single conditions, the multi-condition human motion synthesis remains underexplored. In this study, we propose a multi-condition HMS framework, termed MCM, based on a dual-branch structure composed of a main branch and a control branch. This framework effectively extends the applicability of the diffusion model, which is initially predicated solely on textual conditions, to auditory conditions. This extension encompasses both music-to-dance and co-speech HMS while preserving the intrinsic quality of motion and the capabilities for semantic association inherent in the original model. Furthermore, we propose the implementation of a Transformer-based diffusion model, designated as MWNet, as the main branch. This model adeptly apprehends the spatial intricacies and inter-joint correlations inherent in motion sequences, facilitated by the integration of multi-wise self-attention modules. Extensive experiments show that our method achieves competitive results in single-condition and multi-condition HMS tasks. | [
"['Zeyu Ling' 'Bo Han' 'Yongkang Wongkan' 'Han Lin' 'Mohan Kankanhalli'\n 'Weidong Geng']"
]
|
null | null | 2404.12888 | null | null | http://arxiv.org/pdf/2404.12888v1 | 2024-04-19T13:45:14Z | 2024-04-19T13:45:14Z | Learn2Talk: 3D Talking Face Learns from 2D Talking Face | Speech-driven facial animation methods usually contain two main classes, 3D and 2D talking face, both of which attract considerable research attention in recent years. However, to the best of our knowledge, the research on 3D talking face does not go deeper as 2D talking face, in the aspect of lip-synchronization (lip-sync) and speech perception. To mind the gap between the two sub-fields, we propose a learning framework named Learn2Talk, which can construct a better 3D talking face network by exploiting two expertise points from the field of 2D talking face. Firstly, inspired by the audio-video sync network, a 3D sync-lip expert model is devised for the pursuit of lip-sync between audio and 3D facial motion. Secondly, a teacher model selected from 2D talking face methods is used to guide the training of the audio-to-3D motions regression network to yield more 3D vertex accuracy. Extensive experiments show the advantages of the proposed framework in terms of lip-sync, vertex accuracy and speech perception, compared with state-of-the-arts. Finally, we show two applications of the proposed framework: audio-visual speech recognition and speech-driven 3D Gaussian Splatting based avatar animation. | [
"['Yixiang Zhuang' 'Baoping Cheng' 'Yao Cheng' 'Yuntao Jin' 'Renshuai Liu'\n 'Chengyang Li' 'Xuan Cheng' 'Jing Liao' 'Juncong Lin']"
]
|
null | null | 2404.12892 | null | null | http://arxiv.org/pdf/2404.12892v1 | 2024-04-19T13:51:40Z | 2024-04-19T13:51:40Z | A Machine Learning-Based Error Mitigation Approach For Reliable Software
Development On IBM'S Quantum Computers | Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners. | [
"['Asmar Muqeet' 'Shaukat Ali' 'Tao Yue' 'Paolo Arcaini']"
]
|
null | null | 2404.12899 | null | null | http://arxiv.org/pdf/2404.12899v1 | 2024-04-19T14:11:32Z | 2024-04-19T14:11:32Z | Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins
via Active Learning | Scientific advancement is universally based on the dynamic interplay between theoretical insights, modelling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is not only to use theory but also on-the-fly theory updates during the experiment. Here, we introduce a method for integrating theory into the loop through Bayesian co-navigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Co-navigation/tree/main | [
"['Boris N. Slautin' 'Yongtao Liu' 'Hiroshi Funakubo' 'Rama K. Vasudevan'\n 'Maxim A. Ziatdinov' 'Sergei V. Kalinin']"
]
|
null | null | 2404.12908 | null | null | http://arxiv.org/pdf/2404.12908v1 | 2024-04-19T14:30:41Z | 2024-04-19T14:30:41Z | Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images | Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. However, their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content, raising concerns about digital authenticity and potential misuse in creating deepfakes. This work introduces a robust detection framework that integrates image and text features extracted by CLIP model with a Multilayer Perceptron (MLP) classifier. We propose a novel loss that can improve the detector's robustness and handle imbalanced datasets. Additionally, we flatten the loss landscape during the model training to improve the detector's generalization capabilities. The effectiveness of our method, which outperforms traditional detection techniques, is demonstrated through extensive experiments, underscoring its potential to set a new state-of-the-art approach in DM-generated image detection. The code is available at https://github.com/Purdue-M2/Robust_DM_Generated_Image_Detection. | [
"['Santosh' 'Li Lin' 'Irene Amerini' 'Xin Wang' 'Shu Hu']"
]
|
null | null | 2404.12917 | null | null | http://arxiv.org/pdf/2404.12917v2 | 2024-05-07T12:45:07Z | 2024-04-19T14:42:42Z | Zero-Shot Stitching in Reinforcement Learning using Relative
Representations | Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. However, it is also known that variations in the input (e.g., different colors of the panorama due to the season of the year) or the task (e.g., changing the speed limit for a car to respect) could require complete retraining of the agents. In this work, we leverage recent developments in unifying latent representations to demonstrate that it is possible to combine the components of an agent, rather than retrain it from scratch. We build upon the recent relative representations framework and adapt it for Visual RL. This allows us to create completely new agents capable of handling environment-task combinations never seen during training. Our work paves the road toward a more accessible and flexible use of reinforcement learning. | [
"['Antonio Pio Ricciardi' 'Valentino Maiorca' 'Luca Moschella'\n 'Riccardo Marin' 'Emanuele Rodolà']"
]
|
null | null | 2404.12920 | null | null | http://arxiv.org/pdf/2404.12920v1 | 2024-04-19T14:43:48Z | 2024-04-19T14:43:48Z | Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models | Localizing the exact pathological regions in a given medical scan is an important imaging problem that requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, forms of supervision, such as accompanying free-text reports, which are readily available. The task of performing localization with textual guidance is commonly referred to as phrase grounding. In this work, we use a publicly available Foundation Model, namely the Latent Diffusion Model, to solve this challenging task. This choice is supported by the fact that the Latent Diffusion Model, despite being generative in nature, contains mechanisms (cross-attention) that implicitly align visual and textual features, thus leading to intermediate representations that are suitable for the task at hand. In addition, we aim to perform this task in a zero-shot manner, i.e., without any further training on target data, meaning that the model's weights remain frozen. To this end, we devise strategies to select features and also refine them via post-processing without extra learnable parameters. We compare our proposed method with state-of-the-art approaches which explicitly enforce image-text alignment in a joint embedding space via contrastive learning. Results on a popular chest X-ray benchmark indicate that our method is competitive wih SOTA on different types of pathology, and even outperforms them on average in terms of two metrics (mean IoU and AUC-ROC). Source code will be released upon acceptance. | [
"['Konstantinos Vilouras' 'Pedro Sanchez' \"Alison Q. O'Neil\"\n 'Sotirios A. Tsaftaris']"
]
|
null | null | 2404.12922 | null | null | http://arxiv.org/pdf/2404.12922v1 | 2024-04-19T14:45:27Z | 2024-04-19T14:45:27Z | Is Retain Set All You Need in Machine Unlearning? Restoring Performance
of Unlearned Models with Out-Of-Distribution Images | In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model's test accuracy without using a retain set, which is a key component in state-of-the-art approximate unlearning algorithms. Our approach utilizes a modified Mahalanobis distance to guide the unlearning of the feature vectors of the instances to be forgotten, aligning them to the nearest wrong class distribution. Moreover, we propose a distillation-trick mechanism that distills the knowledge of the original model into the unlearning model with out-of-distribution images for retaining the original model's test performance without using any retain set. Importantly, we propose a self-forget version of SCAR that unlearns without having access to the forget set. We experimentally verified the effectiveness of our method, on three public datasets, comparing it with state-of-the-art methods. Our method obtains performance higher than methods that operate without the retain set and comparable w.r.t the best methods that rely on the retain set. | [
"['Jacopo Bonato' 'Marco Cotogni' 'Luigi Sabetta']"
]
|
null | null | 2404.12923 | null | null | http://arxiv.org/pdf/2404.12923v2 | 2024-04-23T18:07:34Z | 2024-04-19T14:52:14Z | Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System
Identification in Continuous Time | In engineering, accurately modeling nonlinear dynamic systems from data contaminated by noise is both essential and complex. Established Sequential Monte Carlo (SMC) methods, used for the Bayesian identification of these systems, facilitate the quantification of uncertainty in the parameter identification process. A significant challenge in this context is the numerical integration of continuous-time ordinary differential equations (ODEs), crucial for aligning theoretical models with discretely sampled data. This integration introduces additional numerical uncertainty, a factor that is often over looked. To address this issue, the field of probabilistic numerics combines numerical methods, such as numerical integration, with probabilistic modeling to offer a more comprehensive analysis of total uncertainty. By retaining the accuracy of classical deterministic methods, these probabilistic approaches offer a deeper understanding of the uncertainty inherent in the inference process. This paper demonstrates the application of a probabilistic numerical method for solving ODEs in the joint parameter-state identification of nonlinear dynamic systems. The presented approach efficiently identifies latent states and system parameters from noisy measurements. Simultaneously incorporating probabilistic solutions to the ODE in the identification challenge. The methodology's primary advantage lies in its capability to produce posterior distributions over system parameters, thereby representing the inherent uncertainties in both the data and the identification process. | [
"['Joe D. Longbottom' 'Max D. Champneys' 'Timothy J. Rogers']"
]
|
null | null | 2404.12928 | null | null | http://arxiv.org/pdf/2404.12928v1 | 2024-04-19T14:55:21Z | 2024-04-19T14:55:21Z | The Positivity of the Neural Tangent Kernel | The Neural Tangent Kernel (NTK) has emerged as a fundamental concept in the study of wide Neural Networks. In particular, it is known that the positivity of the NTK is directly related to the memorization capacity of sufficiently wide networks, i.e., to the possibility of reaching zero loss in training, via gradient descent. Here we will improve on previous works and obtain a sharp result concerning the positivity of the NTK of feedforward networks of any depth. More precisely, we will show that, for any non-polynomial activation function, the NTK is strictly positive definite. Our results are based on a novel characterization of polynomial functions which is of independent interest. | [
"['Luís Carvalho' 'João L. Costa' 'José Mourão' 'Gonçalo Oliveira']"
]
|
null | null | 2404.12940 | null | null | http://arxiv.org/pdf/2404.12940v2 | 2024-06-01T10:25:54Z | 2024-04-19T15:10:54Z | Neural Flow Diffusion Models: Learnable Forward Process for Improved
Diffusion Modelling | Conventional diffusion models typically relies on a fixed forward process, which implicitly defines complex marginal distributions over latent variables. This can often complicate the reverse process' task in learning generative trajectories, and results in costly inference for diffusion models. To address these limitations, we introduce Neural Flow Diffusion Models (NFDM), a novel framework that enhances diffusion models by supporting a broader range of forward processes beyond the standard Gaussian. We also propose a novel parameterization technique for learning the forward process. Our framework provides an end-to-end, simulation-free optimization objective, effectively minimizing a variational upper bound on the negative log-likelihood. Experimental results demonstrate NFDM's strong performance, evidenced by state-of-the-art likelihood estimation. Furthermore, we investigate NFDM's capacity for learning generative dynamics with specific characteristics, such as deterministic straight lines trajectories, and demonstrate how the framework may be adopted for learning bridges between two distributions. The results underscores NFDM's versatility and its potential for a wide range of applications. | [
"['Grigory Bartosh' 'Dmitry Vetrov' 'Christian A. Naesseth']"
]
|
null | null | 2404.12948 | null | null | http://arxiv.org/pdf/2404.12948v1 | 2024-04-19T15:26:36Z | 2024-04-19T15:26:36Z | Next Generation Loss Function for Image Classification | Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function is crucial to achieve task-specific behaviour and highly influences the capability of the model. A variety of loss functions have been proposed for a wide range of tasks affecting training and model performance. For classification tasks, the cross entropy is the de-facto standard and usually the first choice. Here, we try to experimentally challenge the well-known loss functions, including cross entropy (CE) loss, by utilizing the genetic programming (GP) approach, a population-based evolutionary algorithm. GP constructs loss functions from a set of operators and leaf nodes and these functions are repeatedly recombined and mutated to find an optimal structure. Experiments were carried out on different small-sized datasets CIFAR-10, CIFAR-100 and Fashion-MNIST using an Inception model. The 5 best functions found were evaluated for different model architectures on a set of standard datasets ranging from 2 to 102 classes and very different sizes. One function, denoted as Next Generation Loss (NGL), clearly stood out showing same or better performance for all tested datasets compared to CE. To evaluate the NGL function on a large-scale dataset, we tested its performance on the Imagenet-1k dataset where it showed improved top-1 accuracy compared to models trained with identical settings and other losses. Finally, the NGL was trained on a segmentation downstream task for Pascal VOC 2012 and COCO-Stuff164k datasets improving the underlying model performance. | [
"['Shakhnaz Akhmedova' 'Nils Körber']"
]
|
null | null | 2404.12957 | null | null | http://arxiv.org/pdf/2404.12957v1 | 2024-04-19T15:40:39Z | 2024-04-19T15:40:39Z | Towards Reliable Latent Knowledge Estimation in LLMs: In-Context
Learning vs. Prompting Based Factual Knowledge Extraction | We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs). We leverage the in-context learning (ICL) abilities of LLMs to estimate the extent to which an LLM knows the facts stored in a knowledge base. Our knowledge estimator avoids reliability concerns with previous prompting-based methods, is both conceptually simpler and easier to apply, and we demonstrate that it can surface more of the latent knowledge embedded in LLMs. We also investigate how different design choices affect the performance of ICL-based knowledge estimation. Using the proposed estimator, we perform a large-scale evaluation of the factual knowledge of a variety of open source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set of relations and facts from the Wikidata knowledge base. We observe differences in the factual knowledge between different model families and models of different sizes, that some relations are consistently better known than others but that models differ in the precise facts they know, and differences in the knowledge of base models and their finetuned counterparts. | [
"['Qinyuan Wu' 'Mohammad Aflah Khan' 'Soumi Das' 'Vedant Nanda'\n 'Bishwamittra Ghosh' 'Camila Kolling' 'Till Speicher'\n 'Laurent Bindschaedler' 'Krishna P. Gummadi' 'Evimaria Terzi']"
]
|
null | null | 2404.12958 | null | null | http://arxiv.org/pdf/2404.12958v1 | 2024-04-19T15:40:47Z | 2024-04-19T15:40:47Z | Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images
Utilizing Contrastive Learning and Embedding Similarity | Despite the advancement of deep learning-based computer-aided diagnosis (CAD) methods for pneumonia from adult chest x-ray (CXR) images, the performance of CAD methods applied to pediatric images remains suboptimal, mainly due to the lack of large-scale annotated pediatric imaging datasets. Establishing a proper framework to leverage existing adult large-scale CXR datasets can thus enhance pediatric pneumonia detection performance. In this paper, we propose a three-branch parallel path learning-based framework that utilizes both adult and pediatric datasets to improve the performance of deep learning models on pediatric test datasets. The paths are trained with pediatric only, adult only, and both types of CXRs, respectively. Our proposed framework utilizes the multi-positive contrastive loss to cluster the classwise embeddings and the embedding similarity loss among these three parallel paths to make the classwise embeddings as close as possible to reduce the effect of domain shift. Experimental evaluations on open-access adult and pediatric CXR datasets show that the proposed method achieves a superior AUROC score of 0.8464 compared to 0.8348 obtained using the conventional approach of join training on both datasets. The proposed approach thus paves the way for generalized CAD models that are effective for both adult and pediatric age groups. | [
"['Mohammad Zunaed' 'Anwarul Hasan' 'Taufiq Hasan']"
]
|
null | null | 2404.12968 | null | null | http://arxiv.org/pdf/2404.12968v1 | 2024-04-19T15:54:15Z | 2024-04-19T15:54:15Z | Scalable Data Assimilation with Message Passing | Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes, yet existing approaches suffer from synchronisation overhead in this setting. In this paper, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements. | [
"['Oscar Key' 'So Takao' 'Daniel Giles' 'Marc Peter Deisenroth']"
]
|
null | null | 2404.12973 | null | null | http://arxiv.org/pdf/2404.12973v2 | 2024-05-27T13:43:30Z | 2024-04-19T16:01:00Z | Cross-modal Diffusion Modelling for Super-resolved Spatial
Transcriptomics | The recent advancement of spatial transcriptomics (ST) allows to characterize spatial gene expression within tissue for discovery research. However, current ST platforms suffer from low resolution, hindering in-depth understanding of spatial gene expression. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. However, current super-resolution methods are limited by restoration uncertainty and mode collapse. Although diffusion models have shown promise in capturing complex interactions between multi-modal conditions, it remains a challenge to integrate histology images and gene expression for super-resolved ST maps. This paper proposes a cross-modal conditional diffusion model for super-resolving ST maps with the guidance of histology images. Specifically, we design a multi-modal disentangling network with cross-modal adaptive modulation to utilize complementary information from histology images and spatial gene expression. Moreover, we propose a dynamic cross-attention modelling strategy to extract hierarchical cell-to-tissue information from histology images. Lastly, we propose a co-expression-based gene-correlation graph network to model the co-expression relationship of multiple genes. Experiments show that our method outperforms other state-of-the-art methods in ST super-resolution on three public datasets. | [
"['Xiaofei Wang' 'Xingxu Huang' 'Stephen J. Price' 'Chao Li']"
]
|
null | null | 2404.12979 | null | null | http://arxiv.org/pdf/2404.12979v1 | 2024-04-19T16:09:17Z | 2024-04-19T16:09:17Z | TRNet: Two-level Refinement Network leveraging Speech Enhancement for
Noise Robust Speech Emotion Recognition | One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in diminished SER performance in practical use. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise reduction and noise level estimation. Later, we utilize clean speech spectrograms and their corresponding deep representations as reference signals to refine the spectrogram distortion and representation shift of enhanced speech during model training. Experimental results validate that the proposed TRNet substantially increases the system's robustness in both matched and unmatched noisy environments, without compromising its performance in clean environments. | [
"['Chengxin Chen' 'Pengyuan Zhang']"
]
|
null | null | 2404.12999 | null | null | http://arxiv.org/pdf/2404.12999v1 | 2024-04-19T16:54:55Z | 2024-04-19T16:54:55Z | Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned
Reinforcement Learning | Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards. A primary limitation to exploration efficiency is the agent's inability to leverage environmental structural patterns. In this study, we introduce a novel framework, GEASD, designed to capture these patterns through an adaptive skill distribution during the learning process. This distribution optimizes the local entropy of achieved goals within a contextual horizon, enhancing goal-spreading behaviors and facilitating deep exploration in states containing familiar structural patterns. Our experiments reveal marked improvements in exploration efficiency using the adaptive skill distribution compared to a uniform skill distribution. Additionally, the learned skill distribution demonstrates robust generalization capabilities, achieving substantial exploration progress in unseen tasks containing similar local structures. | [
"['Lisheng Wu' 'Ke Chen']"
]
|
null | null | 2404.13000 | null | null | http://arxiv.org/pdf/2404.13000v1 | 2024-04-19T16:55:12Z | 2024-04-19T16:55:12Z | RadRotator: 3D Rotation of Radiographs with Diffusion Models | Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few previous studies attempted to convert two or more input radiographs into computed tomography (CT) volumes. Following their effort, we introduce a diffusion model-based technology that can rotate the anatomical content of any input radiograph in 3D space, potentially enabling the visualization of the entire anatomical content of the radiograph from any viewpoint in 3D. Similar to previous studies, we used CT volumes to create Digitally Reconstructed Radiographs (DRRs) as the training data for our model. However, we addressed two significant limitations encountered in previous studies: 1. We utilized conditional diffusion models with classifier-free guidance instead of Generative Adversarial Networks (GANs) to achieve higher mode coverage and improved output image quality, with the only trade-off being slower inference time, which is often less critical in medical applications; and 2. We demonstrated that the unreliable output of style transfer deep learning (DL) models, such as Cycle-GAN, to transfer the style of actual radiographs to DRRs could be replaced with a simple yet effective training transformation that randomly changes the pixel intensity histograms of the input and ground-truth imaging data during training. This transformation makes the diffusion model agnostic to any distribution variations of the input data pixel intensity, enabling the reliable training of a DL model on input DRRs and applying the exact same model to conventional radiographs (or DRRs) during inference. | [
"['Pouria Rouzrokh' 'Bardia Khosravi' 'Shahriar Faghani'\n 'Kellen L. Mulford' 'Michael J. Taunton' 'Bradley J. Erickson'\n 'Cody C. Wyles']"
]
|
null | null | 2404.13002 | null | null | http://arxiv.org/pdf/2404.13002v1 | 2024-04-19T16:59:04Z | 2024-04-19T16:59:04Z | Towards Robust Ferrous Scrap Material Classification with Deep Learning
and Conformal Prediction | In the steel production domain, recycling ferrous scrap is essential for environmental and economic sustainability, as it reduces both energy consumption and greenhouse gas emissions. However, the classification of scrap materials poses a significant challenge, requiring advancements in automation technology. Additionally, building trust among human operators is a major obstacle. Traditional approaches often fail to quantify uncertainty and lack clarity in model decision-making, which complicates acceptance. In this article, we describe how conformal prediction can be employed to quantify uncertainty and add robustness in scrap classification. We have adapted the Split Conformal Prediction technique to seamlessly integrate with state-of-the-art computer vision models, such as the Vision Transformer (ViT), Swin Transformer, and ResNet-50, while also incorporating Explainable Artificial Intelligence (XAI) methods. We evaluate the approach using a comprehensive dataset of 8147 images spanning nine ferrous scrap classes. The application of the Split Conformal Prediction method allowed for the quantification of each model's uncertainties, which enhanced the understanding of predictions and increased the reliability of the results. Specifically, the Swin Transformer model demonstrated more reliable outcomes than the others, as evidenced by its smaller average size of prediction sets and achieving an average classification accuracy exceeding 95%. Furthermore, the Score-CAM method proved highly effective in clarifying visual features, significantly enhancing the explainability of the classification decisions. | [
"['Paulo Henrique dos Santos' 'Valéria de Carvalho Santos'\n 'Eduardo José da Silva Luz']"
]
|
null | null | 2404.13013 | null | null | http://arxiv.org/pdf/2404.13013v1 | 2024-04-19T17:22:51Z | 2024-04-19T17:22:51Z | Groma: Localized Visual Tokenization for Grounding Multimodal Large
Language Models | We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability. Beyond holistic image understanding, Groma is adept at region-level tasks such as region captioning and visual grounding. Such capabilities are built upon a localized visual tokenization mechanism, where an image input is decomposed into regions of interest and subsequently encoded into region tokens. By integrating region tokens into user instructions and model responses, we seamlessly enable Groma to understand user-specified region inputs and ground its textual output to images. Besides, to enhance the grounded chat ability of Groma, we curate a visually grounded instruction dataset by leveraging the powerful GPT-4V and visual prompting techniques. Compared with MLLMs that rely on the language model or external module for localization, Groma consistently demonstrates superior performances in standard referring and grounding benchmarks, highlighting the advantages of embedding localization into image tokenization. Project page: https://groma-mllm.github.io/. | [
"['Chuofan Ma' 'Yi Jiang' 'Jiannan Wu' 'Zehuan Yuan' 'Xiaojuan Qi']"
]
|
null | null | 2404.13016 | null | null | http://arxiv.org/pdf/2404.13016v2 | 2024-04-25T03:25:25Z | 2024-04-19T17:25:43Z | Optimizing Calibration by Gaining Aware of Prediction Correctness | Model calibration aims to align confidence with prediction correctness. The Cross-Entropy (CE) loss is widely used for calibrator training, which enforces the model to increase confidence on the ground truth class. However, we find the CE loss has intrinsic limitations. For example, for a narrow misclassification, a calibrator trained by the CE loss often produces high confidence on the wrongly predicted class (e.g., a test sample is wrongly classified and its softmax score on the ground truth class is around 0.4), which is undesirable. In this paper, we propose a new post-hoc calibration objective derived from the aim of calibration. Intuitively, the proposed objective function asks that the calibrator decrease model confidence on wrongly predicted samples and increase confidence on correctly predicted samples. Because a sample itself has insufficient ability to indicate correctness, we use its transformed versions (e.g., rotated, greyscaled and color-jittered) during calibrator training. Trained on an in-distribution validation set and tested with isolated, individual test samples, our method achieves competitive calibration performance on both in-distribution and out-of-distribution test sets compared with the state of the art. Further, our analysis points out the difference between our method and commonly used objectives such as CE loss and mean square error loss, where the latters sometimes deviates from the calibration aim. | [
"['Yuchi Liu' 'Lei Wang' 'Yuli Zou' 'James Zou' 'Liang Zheng']"
]
|
null | null | 2404.13020 | null | null | http://arxiv.org/pdf/2404.13020v1 | 2024-04-19T17:30:10Z | 2024-04-19T17:30:10Z | Stronger Random Baselines for In-Context Learning | Evaluating the in-context learning classification performance of language models poses challenges due to small dataset sizes, extensive prompt-selection using the validation set, and intentionally difficult tasks that lead to near-random performance. The standard random baseline -- the expected accuracy of guessing labels uniformly at random -- is stable when the evaluation set is used only once or when the dataset is large. We account for the common practice of validation set reuse and existing small datasets with a stronger random baseline: the expected maximum accuracy across multiple random classifiers. When choosing the best prompt demonstrations across six quantized language models applied to 16 BIG-bench Lite tasks, more than 20% of the few-shot results that exceed the standard baseline do not exceed this stronger random baseline. When held-out test sets are available, this stronger baseline is also a better predictor of held-out performance than the standard baseline, avoiding unnecessary test set evaluations. This maximum random baseline provides an easily calculated drop-in replacement for the standard baseline. | [
"['Gregory Yauney' 'David Mimno']"
]
|
null | null | 2404.13040 | null | null | http://arxiv.org/pdf/2404.13040v1 | 2024-04-19T17:53:43Z | 2024-04-19T17:53:43Z | Analysis of Classifier-Free Guidance Weight Schedulers | Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional predictions using a fixed weight. However, recent works vary the weights throughout the diffusion process, reporting superior results but without providing any rationale or analysis. By conducting comprehensive experiments, this paper provides insights into CFG weight schedulers. Our findings suggest that simple, monotonically increasing weight schedulers consistently lead to improved performances, requiring merely a single line of code. In addition, more complex parametrized schedulers can be optimized for further improvement, but do not generalize across different models and tasks. | [
"['Xi Wang' 'Nicolas Dufour' 'Nefeli Andreou' 'Marie-Paule Cani'\n 'Victoria Fernandez Abrevaya' 'David Picard' 'Vicky Kalogeiton']"
]
|
null | null | 2404.13043 | null | null | http://arxiv.org/pdf/2404.13043v1 | 2024-04-19T17:57:29Z | 2024-04-19T17:57:29Z | Data Alignment for Zero-Shot Concept Generation in Dermatology AI | AI in dermatology is evolving at a rapid pace but the major limitation to training trustworthy classifiers is the scarcity of data with ground-truth concept level labels, which are meta-labels semantically meaningful to humans. Foundation models like CLIP providing zero-shot capabilities can help alleviate this challenge by leveraging vast amounts of image-caption pairs available on the internet. CLIP can be fine-tuned using domain specific image-caption pairs to improve classification performance. However, CLIP's pre-training data is not well-aligned with the medical jargon that clinicians use to perform diagnoses. The development of large language models (LLMs) in recent years has led to the possibility of leveraging the expressive nature of these models to generate rich text. Our goal is to use these models to generate caption text that aligns well with both the clinical lexicon and with the natural human language used in CLIP's pre-training data. Starting with captions used for images in PubMed articles, we extend them by passing the raw captions through an LLM fine-tuned on the field's several textbooks. We find that using captions generated by an expressive fine-tuned LLM like GPT-3.5 improves downstream zero-shot concept classification performance. | [
"['Soham Gadgil' 'Mahtab Bigverdi']"
]
|
null | null | 2404.13049 | null | null | http://arxiv.org/pdf/2404.13049v2 | 2024-06-19T22:13:46Z | 2024-03-16T00:15:20Z | DG-RePlAce: A Dataflow-Driven GPU-Accelerated Analytical Global
Placement Framework for Machine Learning Accelerators | Global placement is a fundamental step in VLSI physical design. The wide use of 2D processing element (PE) arrays in machine learning accelerators poses new challenges of scalability and Quality of Results (QoR) for state-of-the-art academic global placers. In this work, we develop DG-RePlAce, a new and fast GPU-accelerated global placement framework built on top of the OpenROAD infrastructure, which exploits the inherent dataflow and datapath structures of machine learning accelerators. Experimental results with a variety of machine learning accelerators using a commercial 12nm enablement show that, compared with RePlAce (DREAMPlace), our approach achieves an average reduction in routed wirelength by 10% (7%) and total negative slack (TNS) by 31% (34%), with faster global placement and on-par total runtimes relative to DREAMPlace. Empirical studies on the TILOS MacroPlacement Benchmarks further demonstrate that post-route improvements over RePlAce and DREAMPlace may reach beyond the motivating application to machine learning accelerators. | [
"['Andrew B. Kahng' 'Zhiang Wang']"
]
|
null | null | 2404.13056 | null | null | http://arxiv.org/pdf/2404.13056v1 | 2024-04-08T14:44:21Z | 2024-04-08T14:44:21Z | Variational Bayesian Optimal Experimental Design with Normalizing Flows | Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood. Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by approximating the posterior distributions with variational forms, and then tightens the bound by optimizing its variational parameters. We introduce the use of normalizing flows (NFs) for representing variational distributions in vOED; we call this approach vOED-NFs. Specifically, we adopt NFs with a conditional invertible neural network architecture built from compositions of coupling layers, and enhanced with a summary network for data dimension reduction. We present Monte Carlo estimators to the lower bound along with gradient expressions to enable a gradient-based simultaneous optimization of the variational parameters and the design variables. The vOED-NFs algorithm is then validated in two benchmark problems, and demonstrated on a partial differential equation-governed application of cathodic electrophoretic deposition and an implicit likelihood case with stochastic modeling of aphid population. The findings suggest that a composition of 4--5 coupling layers is able to achieve lower EIG estimation bias, under a fixed budget of forward model runs, compared to previous approaches. The resulting NFs produce approximate posteriors that agree well with the true posteriors, able to capture non-Gaussian and multi-modal features effectively. | [
"['Jiayuan Dong' 'Christian Jacobsen' 'Mehdi Khalloufi' 'Maryam Akram'\n 'Wanjiao Liu' 'Karthik Duraisamy' 'Xun Huan']"
]
|
null | null | 2404.13061 | null | null | http://arxiv.org/pdf/2404.13061v1 | 2024-04-11T20:29:15Z | 2024-04-11T20:29:15Z | FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning | This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with the goal of minimizing wirelength. In addition to our preliminary learning results, we also evaluated a novel decomposition to address the nature of large search space when placing many blocks on a chipboard. Empirical experiments evaluate the effectiveness of the learning and decomposition paradigms on FPGA placement tasks. | [
"['Shang Wang' 'Deepak Ranganatha Sastry Mamillapalli' 'Tianpei Yang'\n 'Matthew E. Taylor']"
]
|
null | null | 2404.13067 | null | null | http://arxiv.org/pdf/2404.13067v1 | 2024-04-13T14:31:24Z | 2024-04-13T14:31:24Z | Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal
Pre-Training Approach | In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU. | [
"['Feihu Jiang' 'Chuan Qin' 'Jingshuai Zhang' 'Kaichun Yao' 'Xi Chen'\n 'Dazhong Shen' 'Chen Zhu' 'Hengshu Zhu' 'Hui Xiong']"
]
|
null | null | 2404.13068 | null | null | http://arxiv.org/pdf/2404.13068v1 | 2024-04-13T19:10:54Z | 2024-04-13T19:10:54Z | SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle
Routing Problem with Drones Using Reinforcement Learning | The Vehicle Routing Problem with Drones (VRPD) seeks to optimize the routing paths for both trucks and drones, where the trucks are responsible for delivering parcels to customer locations, and the drones are dispatched from these trucks for parcel delivery, subsequently being retrieved by the trucks. Given the NP-Hard complexity of VRPD, numerous heuristic approaches have been introduced. However, improving solution quality and reducing computation time remain significant challenges. In this paper, we conduct a comprehensive examination of heuristic methods designed for solving VRPD, distilling and standardizing them into core elements. We then develop a novel reinforcement learning (RL) framework that is seamlessly integrated with the heuristic solution components, establishing a set of universal principles for incorporating the RL framework with heuristic strategies in an aim to improve both the solution quality and computation speed. This integration has been applied to a state-of-the-art heuristic solution for VRPD, showcasing the substantial benefits of incorporating the RL framework. Our evaluation results demonstrated that the heuristic solution incorporated with our RL framework not only elevated the quality of solutions but also achieved rapid computation speeds, especially when dealing with extensive customer locations. | [
"['Navid Mohammad Imran' 'Myounggyu Won']"
]
|
null | null | 2404.13077 | null | null | http://arxiv.org/abs/2404.13077v1 | 2024-04-16T03:39:16Z | 2024-04-16T03:39:16Z | Improving the Capabilities of Large Language Model Based Marketing
Analytics Copilots With Semantic Search And Fine-Tuning | Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be difficult to understand model workings and insights without extensive implementation teams. In principle, recently developed large language models (LLMs), like GPT-4, can be deployed to provide marketing insights, reducing the time and effort required to make critical decisions. In practice, there are substantial challenges that need to be overcome to reliably use such models. We focus on domain-specific question-answering, SQL generation needed for data retrieval, and tabular analysis and show how a combination of semantic search, prompt engineering, and fine-tuning can be applied to dramatically improve the ability of LLMs to execute these tasks accurately. We compare both proprietary models, like GPT-4, and open-source models, like Llama-2-70b, as well as various embedding methods. These models are tested on sample use cases specific to marketing mix modeling and attribution. | [
"['Yilin Gao' 'Sai Kumar Arava' 'Yancheng Li' 'James W. Snyder Jr']"
]
|
null | null | 2404.13078 | null | null | http://arxiv.org/pdf/2404.13078v2 | 2024-04-23T05:15:18Z | 2024-04-16T05:21:47Z | Empowering Interdisciplinary Research with BERT-Based Models: An
Approach Through SciBERT-CNN with Topic Modeling | Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers. Traditional multi-label text classification methods often ignore semantic relationships and fail to address the inherent class imbalances. This paper introduces a novel approach using the SciBERT model and CNNs to systematically categorize academic abstracts from the Elsevier OA CC-BY corpus. We use a multi-segment input strategy that processes abstracts, body text, titles, and keywords obtained via BERT topic modeling through SciBERT. Here, the [CLS] token embeddings capture the contextual representation of each segment, concatenated and processed through a CNN. The CNN uses convolution and pooling to enhance feature extraction and reduce dimensionality, optimizing the data for classification. Additionally, we incorporate class weights based on label frequency to address the class imbalance, significantly improving the classification F1 score and enhancing text classification systems and literature review efficiency. | [
"['Darya Likhareva' 'Hamsini Sankaran' 'Sivakumar Thiyagarajan']"
]
|
null | null | 2404.13079 | null | null | http://arxiv.org/pdf/2404.13079v1 | 2024-04-16T07:27:49Z | 2024-04-16T07:27:49Z | Relational Graph Convolutional Networks for Sentiment Analysis | With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which offer interpretability and flexibility by capturing dependencies between data points represented as nodes in a graph. We demonstrate the effectiveness of our approach by using pre-trained language models such as BERT and RoBERTa with RGCN architecture on product reviews from Amazon and Digikala datasets and evaluating the results. Our experiments highlight the effectiveness of RGCNs in capturing relational information for sentiment analysis tasks. | [
"['Asal Khosravi' 'Zahed Rahmati' 'Ali Vefghi']"
]
|
null | null | 2404.13081 | null | null | http://arxiv.org/pdf/2404.13081v1 | 2024-04-17T01:15:54Z | 2024-04-17T01:15:54Z | SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA
of LLMs | Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs. Augmenting retrieved passages via prompting has the potential to address this limitation, but this direction has been limitedly explored. To this end, we design a simple yet effective framework to enhance open-domain QA (ODQA) with LLMs, based on the summarized retrieval (SuRe). SuRe helps LLMs predict more accurate answers for a given question, which are well-supported by the summarized retrieval that could be viewed as an explicit rationale extracted from the retrieved passages. Specifically, SuRe first constructs summaries of the retrieved passages for each of the multiple answer candidates. Then, SuRe confirms the most plausible answer from the candidate set by evaluating the validity and ranking of the generated summaries. Experimental results on diverse ODQA benchmarks demonstrate the superiority of SuRe, with improvements of up to 4.6% in exact match (EM) and 4.0% in F1 score over standard prompting approaches. SuRe also can be integrated with a broad range of retrieval methods and LLMs. Finally, the generated summaries from SuRe show additional advantages to measure the importance of retrieved passages and serve as more preferred rationales by models and humans. | [
"['Jaehyung Kim' 'Jaehyun Nam' 'Sangwoo Mo' 'Jongjin Park' 'Sang-Woo Lee'\n 'Minjoon Seo' 'Jung-Woo Ha' 'Jinwoo Shin']"
]
|
null | null | 2404.13082 | null | null | http://arxiv.org/pdf/2404.13082v1 | 2024-04-17T05:56:49Z | 2024-04-17T05:56:49Z | TREACLE: Thrifty Reasoning via Context-Aware LLM and Prompt Selection | Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question (i.e., the specific prompt). At the same time, users often have a limit on monetary budget and latency to answer all their questions, and they do not know which LLMs to choose for each question to meet their accuracy and long-term budget requirements. To navigate this rich design space, we propose TREACLE (Thrifty Reasoning via Context-Aware LLM and Prompt Selection), a reinforcement learning policy that jointly selects the model and prompting scheme while respecting the user's monetary cost and latency constraints. TREACLE uses the problem context, including question text embeddings (reflecting the type or difficulty of a query) and the response history (reflecting the consistency of previous responses) to make smart decisions. Our evaluations on standard reasoning datasets (GSM8K, CSQA, and LLC ) with various LLMs and prompts show that TREACLE enables cost savings of up to 85% compared to baselines while maintaining high accuracy. Importantly, it provides the user with the ability to gracefully trade off accuracy for cost. | [
"['Xuechen Zhang' 'Zijian Huang' 'Ege Onur Taga' 'Carlee Joe-Wong'\n 'Samet Oymak' 'Jiasi Chen']"
]
|
null | null | 2404.13087 | null | null | http://arxiv.org/pdf/2404.13087v1 | 2024-04-17T19:53:59Z | 2024-04-17T19:53:59Z | Demystifying Legalese: An Automated Approach for Summarizing and
Analyzing Overlaps in Privacy Policies and Terms of Service | The complexities of legalese in terms and policy documents can bind individuals to contracts they do not fully comprehend, potentially leading to uninformed data sharing. Our work seeks to alleviate this issue by developing language models that provide automated, accessible summaries and scores for such documents, aiming to enhance user understanding and facilitate informed decisions. We compared transformer-based and conventional models during training on our dataset, and RoBERTa performed better overall with a remarkable 0.74 F1-score. Leveraging our best-performing model, RoBERTa, we highlighted redundancies and potential guideline violations by identifying overlaps in GDPR-required documents, underscoring the necessity for stricter GDPR compliance. | [
"['Shikha Soneji' 'Mitchell Hoesing' 'Sujay Koujalgi' 'Jonathan Dodge']"
]
|
null | null | 2404.13096 | null | null | http://arxiv.org/pdf/2404.13096v1 | 2024-04-19T06:13:37Z | 2024-04-19T06:13:37Z | Reducing Redundant Computation in Multi-Agent Coordination through
Locally Centralized Execution | In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the redundant computation problem. This occurs when multiple agents redundantly perform the same or similar computation due to overlapping observations. To address this issue, this study introduces a novel method referred to as locally centralized team transformer (LCTT). LCTT establishes a locally centralized execution framework where selected agents serve as leaders, issuing instructions, while the rest agents, designated as workers, act as these instructions without activating their policy networks. For LCTT, we proposed the team-transformer (T-Trans) architecture that allows leaders to provide specific instructions to each worker, and the leadership shift mechanism that allows agents autonomously decide their roles as leaders or workers. Our experimental results demonstrate that the proposed method effectively reduces redundant computation, does not decrease reward levels, and leads to faster learning convergence. | [
"['Yidong Bai' 'Toshiharu Sugawara']"
]
|
null | null | 2404.13097 | null | null | http://arxiv.org/pdf/2404.13097v1 | 2024-04-19T06:52:57Z | 2024-04-19T06:52:57Z | DISC: Latent Diffusion Models with Self-Distillation from Separated
Conditions for Prostate Cancer Grading | Latent Diffusion Models (LDMs) can generate high-fidelity images from noise, offering a promising approach for augmenting histopathology images for training cancer grading models. While previous works successfully generated high-fidelity histopathology images using LDMs, the generation of image tiles to improve prostate cancer grading has not yet been explored. Additionally, LDMs face challenges in accurately generating admixtures of multiple cancer grades in a tile when conditioned by a tile mask. In this study, we train specific LDMs to generate synthetic tiles that contain multiple Gleason Grades (GGs) by leveraging pixel-wise annotations in input tiles. We introduce a novel framework named Self-Distillation from Separated Conditions (DISC) that generates GG patterns guided by GG masks. Finally, we deploy a training framework for pixel-level and slide-level prostate cancer grading, where synthetic tiles are effectively utilized to improve the cancer grading performance of existing models. As a result, this work surpasses previous works in two domains: 1) our LDMs enhanced with DISC produce more accurate tiles in terms of GG patterns, and 2) our training scheme, incorporating synthetic data, significantly improves the generalization of the baseline model for prostate cancer grading, particularly in challenging cases of rare GG5, demonstrating the potential of generative models to enhance cancer grading when data is limited. | [
"['Man M. Ho' 'Elham Ghelichkhan' 'Yosep Chong' 'Yufei Zhou'\n 'Beatrice Knudsen' 'Tolga Tasdizen']"
]
|
null | null | 2404.13098 | null | null | http://arxiv.org/pdf/2404.13098v1 | 2024-04-19T07:28:51Z | 2024-04-19T07:28:51Z | Implementing Hottopixx Methods for Endmember Extraction in Hyperspectral
Images | Hyperspectral imaging technology has a wide range of applications, including forest management, mineral resource exploration, and Earth surface monitoring. Endmember extraction of hyperspectral images is a key step in leveraging this technology for applications. It aims to identifying the spectral signatures of materials, i.e., the major components in the observed scenes. Theoretically speaking, Hottopixx methods should be effective on problems involving extracting endmembers from hyperspectral images. Yet, these methods are challenging to perform in practice, due to high computational costs. They require us to solve LP problems, called Hottopixx models, whose size grows quadratically with the number of pixels in the image. It is thus still unclear as to whether they are actually effective or not. This study clarifies this situation. We propose an efficient and effective implementation of Hottopixx. Our implementation follows the framework of column generation, which is known as a classical but powerful means of solving large-scale LPs. We show in experiments that our implementation is applicable to the endmember extraction from real hyperspectral images and can provide estimations of endmember signatures with higher accuracy than the existing methods can. | [
"['Tomohiko Mizutani']"
]
|
null | null | 2404.13101 | null | null | http://arxiv.org/pdf/2404.13101v1 | 2024-04-19T09:52:32Z | 2024-04-19T09:52:32Z | DensePANet: An improved generative adversarial network for photoacoustic
tomography image reconstruction from sparse data | Image reconstruction is an essential step of every medical imaging method, including Photoacoustic Tomography (PAT), which is a promising modality of imaging, that unites the benefits of both ultrasound and optical imaging methods. Reconstruction of PAT images using conventional methods results in rough artifacts, especially when applied directly to sparse PAT data. In recent years, generative adversarial networks (GANs) have shown a powerful performance in image generation as well as translation, rendering them a smart choice to be applied to reconstruction tasks. In this study, we proposed an end-to-end method called DensePANet to solve the problem of PAT image reconstruction from sparse data. The proposed model employs a novel modification of UNet in its generator, called FD-UNet++, which considerably improves the reconstruction performance. We evaluated the method on various in-vivo and simulated datasets. Quantitative and qualitative results show the better performance of our model over other prevalent deep learning techniques. | [
"['Hesam Hakimnejad' 'Zohreh Azimifar' 'Narjes Goshtasbi']"
]
|
null | null | 2404.13103 | null | null | http://arxiv.org/pdf/2404.13103v1 | 2024-04-19T11:27:56Z | 2024-04-19T11:27:56Z | ToNNO: Tomographic Reconstruction of a Neural Network's Output for
Weakly Supervised Segmentation of 3D Medical Images | Annotating lots of 3D medical images for training segmentation models is time-consuming. The goal of weakly supervised semantic segmentation is to train segmentation models without using any ground truth segmentation masks. Our work addresses the case where only image-level categorical labels, indicating the presence or absence of a particular region of interest (such as tumours or lesions), are available. Most existing methods rely on class activation mapping (CAM). We propose a novel approach, ToNNO, which is based on the Tomographic reconstruction of a Neural Network's Output. Our technique extracts stacks of slices with different angles from the input 3D volume, feeds these slices to a 2D encoder, and applies the inverse Radon transform in order to reconstruct a 3D heatmap of the encoder's predictions. This generic method allows to perform dense prediction tasks on 3D volumes using any 2D image encoder. We apply it to weakly supervised medical image segmentation by training the 2D encoder to output high values for slices containing the regions of interest. We test it on four large scale medical image datasets and outperform 2D CAM methods. We then extend ToNNO by combining tomographic reconstruction with CAM methods, proposing Averaged CAM and Tomographic CAM, which obtain even better results. | [
"['Marius Schmidt-Mengin' 'Alexis Benichoux' 'Shibeshih Belachew'\n 'Nikos Komodakis' 'Nikos Paragios']"
]
|
null | null | 2404.13105 | null | null | http://arxiv.org/pdf/2404.13105v1 | 2024-04-19T13:50:30Z | 2024-04-19T13:50:30Z | On-Demand Earth System Data Cubes | Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial Intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range. | [
"['David Montero' 'César Aybar' 'Chaonan Ji' 'Guido Kraemer'\n 'Maximilian Söchting' 'Khalil Teber' 'Miguel D. Mahecha']"
]
|
null | null | 2404.13125 | null | null | http://arxiv.org/pdf/2404.13125v1 | 2024-04-19T18:28:38Z | 2024-04-19T18:28:38Z | Towards Robust Real-Time Hardware-based Mobile Malware Detection using
Multiple Instance Learning Formulation | This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance Learning (MIL) approach. We address the mislabeling issue in real-time HMDs, where benign segments in malware time-series incorrectly inherit malware labels, leading to increased false positives. Utilizing the proposed Malicious Discriminative Score within the MIL framework, RT-HMD effectively identifies localized malware behaviors, thereby improving the predictive accuracy. Empirical analysis, using a hardware telemetry dataset collected from a mobile platform across 723 benign and 1033 malware samples, shows a 5% precision boost while maintaining recall, outperforming baselines affected by mislabeled benign segments. | [
"['Harshit Kumar' 'Sudarshan Sharma' 'Biswadeep Chakraborty'\n 'Saibal Mukhopadhyay']"
]
|
null | null | 2404.13131 | null | null | http://arxiv.org/abs/2404.13131v1 | 2024-04-19T18:36:14Z | 2024-04-19T18:36:14Z | From Model Performance to Claim: How a Change of Focus in Machine
Learning Replicability Can Help Bridge the Responsibility Gap | Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication. | [
"['Tianqi Kou']"
]
|
null | null | 2404.13134 | null | null | http://arxiv.org/pdf/2404.13134v1 | 2024-04-19T18:52:07Z | 2024-04-19T18:52:07Z | Deep Learning-based Text-in-Image Watermarking | In this work, we introduce a novel deep learning-based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to enhance data security and integrity. Leveraging the capabilities of deep learning, specifically through the use of Transformer-based architectures for text processing and Vision Transformers for image feature extraction, our method sets new benchmarks in the domain. The proposed method represents the first application of deep learning in text-in-image watermarking that improves adaptivity, allowing the model to intelligently adjust to specific image characteristics and emerging threats. Through testing and evaluation, our method has demonstrated superior robustness compared to traditional watermarking techniques, achieving enhanced imperceptibility that ensures the watermark remains undetectable across various image contents. | [
"['Bishwa Karki' 'Chun-Hua Tsai' 'Pei-Chi Huang' 'Xin Zhong']"
]
|
null | null | 2404.13139 | null | null | http://arxiv.org/pdf/2404.13139v1 | 2024-04-19T18:56:46Z | 2024-04-19T18:56:46Z | Explainable AI for Fair Sepsis Mortality Predictive Model | Artificial intelligence supports healthcare professionals with predictive modeling, greatly transforming clinical decision-making. This study addresses the crucial need for fairness and explainability in AI applications within healthcare to ensure equitable outcomes across diverse patient demographics. By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness. Our method also introduces a novel permutation-based feature importance algorithm aiming at elucidating the contribution of each feature in enhancing fairness on predictions. Unlike existing explainability methods concentrating on explaining feature contribution to predictive performance, our proposed method uniquely bridges the gap in understanding how each feature contributes to fairness. This advancement is pivotal, given sepsis's significant mortality rate and its role in one-third of hospital deaths. Our method not only aids in identifying and mitigating biases within the predictive model but also fosters trust among healthcare stakeholders by improving the transparency and fairness of model predictions, thereby contributing to more equitable and trustworthy healthcare delivery. | [
"['Chia-Hsuan Chang' 'Xiaoyang Wang' 'Christopher C. Yang']"
]
|
null | null | 2404.13142 | null | null | http://arxiv.org/pdf/2404.13142v1 | 2024-04-19T19:03:33Z | 2024-04-19T19:03:33Z | Decentralized Coordination of Distributed Energy Resources through Local
Energy Markets and Deep Reinforcement Learning | As the energy landscape evolves toward sustainability, the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid. One significant aspect of this issue is the notable increase in net load variability at the grid edge. Transactive energy, implemented through local energy markets, has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized, indirect demand response on a community level. Given the nature of these challenges, model-free control approaches, such as deep reinforcement learning, show promise for the decentralized automation of participation within this context. Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics, overlooking the crucial goal of reducing community-level net load variability. This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in ALEX, an economy-driven local energy market. In this setting, agents do not share information and only prioritize individual bill optimization. The study unveils a clear correlation between bill reduction and reduced net load variability in this setup. The impact on net load variability is assessed over various time horizons using metrics such as ramping rate, daily and monthly load factor, as well as daily average and total peak export and import on an open-source dataset. Agents are then benchmarked against several baselines, with their performance levels showing promising results, approaching those of a near-optimal dynamic programming benchmark. | [
"['Daniel May' 'Matthew Taylor' 'Petr Musilek']"
]
|
null | null | 2404.13147 | null | null | http://arxiv.org/pdf/2404.13147v1 | 2024-04-19T19:25:10Z | 2024-04-19T19:25:10Z | Multiclass ROC | Model evaluation is of crucial importance in modern statistics application. The construction of ROC and calculation of AUC have been widely used for binary classification evaluation. Recent research generalizing the ROC/AUC analysis to multi-class classification has problems in at least one of the four areas: 1. failure to provide sensible plots 2. being sensitive to imbalanced data 3. unable to specify mis-classification cost and 4. unable to provide evaluation uncertainty quantification. Borrowing from a binomial matrix factorization model, we provide an evaluation metric summarizing the pair-wise multi-class True Positive Rate (TPR) and False Positive Rate (FPR) with one-dimensional vector representation. Visualization on the representation vector measures the relative speed of increment between TPR and FPR across all the classes pairs, which in turns provides a ROC plot for the multi-class counterpart. An integration over those factorized vector provides a binary AUC-equivalent summary on the classifier performance. Mis-clasification weights specification and bootstrapped confidence interval are also enabled to accommodate a variety of of evaluation criteria. To support our findings, we conducted extensive simulation studies and compared our method to the pair-wise averaged AUC statistics on benchmark datasets. | [
"['Liang Wang' 'Luis Carvalho']"
]
|
null | null | 2404.13150 | null | null | http://arxiv.org/pdf/2404.13150v1 | 2024-04-19T19:41:00Z | 2024-04-19T19:41:00Z | Transformer Based Planning in the Observation Space with Applications to
Trick Taking Card Games | Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large. This challenge is particularly evident in trick-taking card games. While state sampling techniques such as Perfect Information Monte Carlo (PIMC) search has shown success in these contexts, they still have major limitations. We present Generative Observation Monte Carlo Tree Search (GO-MCTS), which utilizes MCTS on observation sequences generated by a game specific model. This method performs the search within the observation space and advances the search using a model that depends solely on the agent's observations. Additionally, we demonstrate that transformers are well-suited as the generative model in this context, and we demonstrate a process for iteratively training the transformer via population-based self-play. The efficacy of GO-MCTS is demonstrated in various games of imperfect information, such as Hearts, Skat, and "The Crew: The Quest for Planet Nine," with promising results. | [
"['Douglas Rebstock' 'Christopher Solinas' 'Nathan R. Sturtevant'\n 'Michael Buro']"
]
|
null | null | 2404.13159 | null | null | http://arxiv.org/pdf/2404.13159v1 | 2024-04-19T19:55:15Z | 2024-04-19T19:55:15Z | Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting | Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration. The conventional technology for HSI in remote sensing applications is based on the push-broom scanning approach in which the camera records the spectral image of a stripe of the scene at a time, while the image is generated by the aggregation of measurements through time. In real-world airborne and spaceborne HSI instruments, some empty stripes would appear at certain locations, because platforms do not always maintain a constant programmed attitude, or have access to accurate digital elevation maps (DEM), and the travelling track is not necessarily aligned with the hyperspectral cameras at all times. This makes the enhancement of the acquired HS images from incomplete or corrupted observations an essential task. We introduce a novel HSI inpainting algorithm here, called Hyperspectral Equivariant Imaging (Hyper-EI). Hyper-EI is a self-supervised learning-based method which does not require training on extensive datasets or access to a pre-trained model. Experimental results show that the proposed method achieves state-of-the-art inpainting performance compared to the existing methods. | [
"['Shuo Li' 'Mike Davies' 'Mehrdad Yaghoobi']"
]
|
null | null | 2404.13161 | null | null | http://arxiv.org/pdf/2404.13161v1 | 2024-04-19T20:11:12Z | 2024-04-19T20:11:12Z | CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large
Language Models | Large language models (LLMs) introduce new security risks, but there are few comprehensive evaluation suites to measure and reduce these risks. We present BenchmarkName, a novel benchmark to quantify LLM security risks and capabilities. We introduce two new areas for testing: prompt injection and code interpreter abuse. We evaluated multiple state-of-the-art (SOTA) LLMs, including GPT-4, Mistral, Meta Llama 3 70B-Instruct, and Code Llama. Our results show that conditioning away risk of attack remains an unsolved problem; for example, all tested models showed between 26% and 41% successful prompt injection tests. We further introduce the safety-utility tradeoff: conditioning an LLM to reject unsafe prompts can cause the LLM to falsely reject answering benign prompts, which lowers utility. We propose quantifying this tradeoff using False Refusal Rate (FRR). As an illustration, we introduce a novel test set to quantify FRR for cyberattack helpfulness risk. We find many LLMs able to successfully comply with "borderline" benign requests while still rejecting most unsafe requests. Finally, we quantify the utility of LLMs for automating a core cybersecurity task, that of exploiting software vulnerabilities. This is important because the offensive capabilities of LLMs are of intense interest; we quantify this by creating novel test sets for four representative problems. We find that models with coding capabilities perform better than those without, but that further work is needed for LLMs to become proficient at exploit generation. Our code is open source and can be used to evaluate other LLMs. | [
"['Manish Bhatt' 'Sahana Chennabasappa' 'Yue Li' 'Cyrus Nikolaidis'\n 'Daniel Song' 'Shengye Wan' 'Faizan Ahmad' 'Cornelius Aschermann'\n 'Yaohui Chen' 'Dhaval Kapil' 'David Molnar' 'Spencer Whitman'\n 'Joshua Saxe']"
]
|
null | null | 2404.13182 | null | null | http://arxiv.org/pdf/2404.13182v1 | 2024-04-19T21:13:18Z | 2024-04-19T21:13:18Z | Spectral Convolutional Conditional Neural Processes | Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes. Their capability to furnish well-calibrated predictions, combined with simple maximum-likelihood training, has established them as appealing solutions for addressing various learning problems, with a particular emphasis on meta-learning. A prominent member of this family, Convolutional Conditional Neural Processes (ConvCNPs), utilizes convolution to explicitly introduce translation equivariance as an inductive bias. However, ConvCNP's reliance on local discrete kernels in its convolution layers can pose challenges in capturing long-range dependencies and complex patterns within the data, especially when dealing with limited and irregularly sampled observations from a new task. Building on the successes of Fourier neural operators (FNOs) for approximating the solution operators of parametric partial differential equations (PDEs), we propose Spectral Convolutional Conditional Neural Processes (SConvCNPs), a new addition to the NPs family that allows for more efficient representation of functions in the frequency domain. | [
"['Peiman Mohseni' 'Nick Duffield']"
]
|
null | null | 2404.13194 | null | null | http://arxiv.org/pdf/2404.13194v1 | 2024-04-19T21:54:20Z | 2024-04-19T21:54:20Z | Privacy-Preserving Debiasing using Data Augmentation and Machine
Unlearning | Data augmentation is widely used to mitigate data bias in the training dataset. However, data augmentation exposes machine learning models to privacy attacks, such as membership inference attacks. In this paper, we propose an effective combination of data augmentation and machine unlearning, which can reduce data bias while providing a provable defense against known attacks. Specifically, we maintain the fairness of the trained model with diffusion-based data augmentation, and then utilize multi-shard unlearning to remove identifying information of original data from the ML model for protection against privacy attacks. Experimental evaluation across diverse datasets demonstrates that our approach can achieve significant improvements in bias reduction as well as robustness against state-of-the-art privacy attacks. | [
"['Zhixin Pan' 'Emma Andrews' 'Laura Chang' 'Prabhat Mishra']"
]
|
null | null | 2404.13198 | null | null | http://arxiv.org/pdf/2404.13198v1 | 2024-04-19T22:13:12Z | 2024-04-19T22:13:12Z | An economically-consistent discrete choice model with flexible utility
specification based on artificial neural networks | Random utility maximisation (RUM) models are one of the cornerstones of discrete choice modelling. However, specifying the utility function of RUM models is not straightforward and has a considerable impact on the resulting interpretable outcomes and welfare measures. In this paper, we propose a new discrete choice model based on artificial neural networks (ANNs) named "Alternative-Specific and Shared weights Neural Network (ASS-NN)", which provides a further balance between flexible utility approximation from the data and consistency with two assumptions: RUM theory and fungibility of money (i.e., "one euro is one euro"). Therefore, the ASS-NN can derive economically-consistent outcomes, such as marginal utilities or willingness to pay, without explicitly specifying the utility functional form. Using a Monte Carlo experiment and empirical data from the Swissmetro dataset, we show that ASS-NN outperforms (in terms of goodness of fit) conventional multinomial logit (MNL) models under different utility specifications. Furthermore, we show how the ASS-NN is used to derive marginal utilities and willingness to pay measures. | [
"['Jose Ignacio Hernandez' 'Niek Mouter' 'Sander van Cranenburgh']"
]
|
null | null | 2404.13207 | null | null | http://arxiv.org/pdf/2404.13207v2 | 2024-05-20T19:10:35Z | 2024-04-19T22:54:54Z | STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge
Bases | Answering real-world complex queries, such as complex product search, often requires accurate retrieval from semi-structured knowledge bases that involve blend of unstructured (e.g., textual descriptions of products) and structured (e.g., entity relations of products) information. However, previous works have mostly studied textual and relational retrieval tasks as separate topics. To address the gap, we develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Relational K nowledge Bases. Our benchmark covers three domains/datasets: product search, academic paper search, and queries in precision medicine. We design a novel pipeline to synthesize realistic user queries that integrate diverse relational information and complex textual properties, together with their ground-truth answers (items). We conduct rigorous human evaluation to validate the quality of our synthesized queries. We further enhance the benchmark with high-quality human-generated queries to provide an authentic reference. STARK serves as a comprehensive testbed for evaluating the performance of retrieval systems driven by large language models (LLMs). Our experiments suggest that STARK presents significant challenges to the current retrieval and LLM systems, indicating the demand for building more capable retrieval systems. The benchmark data and code are available on https://github.com/snap-stanford/stark. | [
"['Shirley Wu' 'Shiyu Zhao' 'Michihiro Yasunaga' 'Kexin Huang' 'Kaidi Cao'\n 'Qian Huang' 'Vassilis N. Ioannidis' 'Karthik Subbian' 'James Zou'\n 'Jure Leskovec']"
]
|
null | null | 2404.13208 | null | null | http://arxiv.org/pdf/2404.13208v1 | 2024-04-19T22:55:23Z | 2024-04-19T22:55:23Z | The Instruction Hierarchy: Training LLMs to Prioritize Privileged
Instructions | Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts. In this work, we argue that one of the primary vulnerabilities underlying these attacks is that LLMs often consider system prompts (e.g., text from an application developer) to be the same priority as text from untrusted users and third parties. To address this, we propose an instruction hierarchy that explicitly defines how models should behave when instructions of different priorities conflict. We then propose a data generation method to demonstrate this hierarchical instruction following behavior, which teaches LLMs to selectively ignore lower-privileged instructions. We apply this method to GPT-3.5, showing that it drastically increases robustness -- even for attack types not seen during training -- while imposing minimal degradations on standard capabilities. | [
"['Eric Wallace' 'Kai Xiao' 'Reimar Leike' 'Lilian Weng'\n 'Johannes Heidecke' 'Alex Beutel']"
]
|
null | null | 2404.13215 | null | null | http://arxiv.org/pdf/2404.13215v1 | 2024-04-19T23:39:56Z | 2024-04-19T23:39:56Z | Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic
Chiral Metamaterials | There has been significant recent interest in the mechanics community to design structures that can either violate reciprocity, or exhibit elastic asymmetry or odd elasticity. While these properties are highly desirable to enable mechanical metamaterials to exhibit novel wave propagation phenomena, it remains an open question as to how to design passive structures that exhibit both significant non-reciprocity and elastic asymmetry. In this paper, we first define several design spaces for chiral metamaterials leveraging specific design parameters, including the ligament contact angles, the ligament shape, and circle radius. Having defined the design spaces, we then leverage machine learning approaches, and specifically Bayesian optimization, to determine optimally performing designs within each design space satisfying maximal non-reciprocity or stiffness asymmetry. Finally, we perform multi-objective optimization by determining the Pareto optimum and find chiral metamaterials that simultaneously exhibit high non-reciprocity and stiffness asymmetry. Our analysis of the underlying mechanisms reveals that chiral metamaterials that can display multiple different contact states under loading in different directions are able to simultaneously exhibit both high non-reciprocity and stiffness asymmetry. Overall, this work demonstrates the effectiveness of employing ML to bring insights to a novel domain with limited prior information, and more generally will pave the way for metamaterials with unique properties and functionality in directing and guiding mechanical wave energy. | [
"['Lingxiao Yuan' 'Emma Lejeune' 'Harold S. Park']"
]
|
null | null | 2404.13218 | null | null | http://arxiv.org/pdf/2404.13218v1 | 2024-04-19T23:54:32Z | 2024-04-19T23:54:32Z | On the Temperature of Machine Learning Systems | We develop a thermodynamic theory for machine learning (ML) systems. Similar to physical thermodynamic systems which are characterized by energy and entropy, ML systems possess these characteristics as well. This comparison inspire us to integrate the concept of temperature into ML systems grounded in the fundamental principles of thermodynamics, and establish a basic thermodynamic framework for machine learning systems with non-Boltzmann distributions. We introduce the concept of states within a ML system, identify two typical types of state, and interpret model training and refresh as a process of state phase transition. We consider that the initial potential energy of a ML system is described by the model's loss functions, and the energy adheres to the principle of minimum potential energy. For a variety of energy forms and parameter initialization methods, we derive the temperature of systems during the phase transition both analytically and asymptotically, highlighting temperature as a vital indicator of system data distribution and ML training complexity. Moreover, we perceive deep neural networks as complex heat engines with both global temperature and local temperatures in each layer. The concept of work efficiency is introduced within neural networks, which mainly depends on the neural activation functions. We then classify neural networks based on their work efficiency, and describe neural networks as two types of heat engines. | [
"['Dong Zhang']"
]
|
null | null | 2404.13220 | null | null | http://arxiv.org/pdf/2404.13220v2 | 2024-04-23T18:21:00Z | 2024-04-20T00:36:54Z | Security and Privacy Product Inclusion | In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds. We propose a threat modeling approach to identify potential risks and countermeasures for product inclusion in security and privacy. We discuss various factors that can affect a user's ability to achieve a high level of security and privacy, including low-income demographics, poor connectivity, shared device usage, ML fairness, etc. We present results from a global security and privacy user experience survey and discuss the implications for product developers. Our work highlights the need for a more inclusive approach to security and privacy and provides a framework for researchers and practitioners to consider when designing products and services for a diverse range of users. | [
"['Dave Kleidermacher' 'Emmanuel Arriaga' 'Eric Wang' 'Sebastian Porst'\n 'Roger Piqueras Jover']"
]
|
null | null | 2404.13224 | null | null | http://arxiv.org/pdf/2404.13224v1 | 2024-04-20T01:14:19Z | 2024-04-20T01:14:19Z | Model-Based Counterfactual Explanations Incorporating Feature Space
Attributes for Tabular Data | Machine-learning models, which are known to accurately predict patterns from large datasets, are crucial in decision making. Consequently, counterfactual explanations-methods explaining predictions by introducing input perturbations-have become prominent. These perturbations often suggest ways to alter the predictions, leading to actionable recommendations. However, the current techniques require resolving the optimization problems for each input change, rendering them computationally expensive. In addition, traditional encoding methods inadequately address the perturbations of categorical variables in tabular data. Thus, this study propose FastDCFlow, an efficient counterfactual explanation method using normalizing flows. The proposed method captures complex data distributions, learns meaningful latent spaces that retain proximity, and improves predictions. For categorical variables, we employed TargetEncoding, which respects ordinal relationships and includes perturbation costs. The proposed method outperformed existing methods in multiple metrics, striking a balance between trade offs for counterfactual explanations. The source code is available in the following repository: https://github.com/sumugit/FastDCFlow. | [
"['Yuta Sumiya' 'Hayaru shouno']"
]
|
null | null | 2404.13235 | null | null | http://arxiv.org/pdf/2404.13235v1 | 2024-04-20T02:12:59Z | 2024-04-20T02:12:59Z | TrialDura: Hierarchical Attention Transformer for Interpretable Clinical
Trial Duration Prediction | The clinical trial process, also known as drug development, is an indispensable step toward the development of new treatments. The major objective of interventional clinical trials is to assess the safety and effectiveness of drug-based treatment in treating certain diseases in the human body. However, clinical trials are lengthy, labor-intensive, and costly. The duration of a clinical trial is a crucial factor that influences overall expenses. Therefore, effective management of the timeline of a clinical trial is essential for controlling the budget and maximizing the economic viability of the research. To address this issue, We propose TrialDura, a machine learning-based method that estimates the duration of clinical trials using multimodal data, including disease names, drug molecules, trial phases, and eligibility criteria. Then, we encode them into Bio-BERT embeddings specifically tuned for biomedical contexts to provide a deeper and more relevant semantic understanding of clinical trial data. Finally, the model's hierarchical attention mechanism connects all of the embeddings to capture their interactions and predict clinical trial duration. Our proposed model demonstrated superior performance with a mean absolute error (MAE) of 1.04 years and a root mean square error (RMSE) of 1.39 years compared to the other models, indicating more accurate clinical trial duration prediction. Publicly available code can be found at https://anonymous.4open.science/r/TrialDura-F196 | [
"['Ling Yue' 'Jonathan Li' 'Md Zabirul Islam' 'Bolun Xia' 'Tianfan Fu'\n 'Jintai Chen']"
]
|
null | null | 2404.13238 | null | null | http://arxiv.org/pdf/2404.13238v1 | 2024-04-20T02:30:21Z | 2024-04-20T02:30:21Z | Personalized Wireless Federated Learning for Large Language Models | Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their deployment in wireless networks still face challenges, i.e., a lack of privacy and security protection mechanisms. Federated Learning (FL) has emerged as a promising approach to address these challenges. Yet, it suffers from issues including inefficient handling with big and heterogeneous data, resource-intensive training, and high communication overhead. To tackle these issues, we first compare different learning stages and their features of LLMs in wireless networks. Next, we introduce two personalized wireless federated fine-tuning methods with low communication overhead, i.e., (1) Personalized Federated Instruction Tuning (PFIT), which employs reinforcement learning to fine-tune local LLMs with diverse reward models to achieve personalization; (2) Personalized Federated Task Tuning (PFTT), which can leverage global adapters and local Low-Rank Adaptations (LoRA) to collaboratively fine-tune local LLMs, where the local LoRAs can be applied to achieve personalization without aggregation. Finally, we perform simulations to demonstrate the effectiveness of the proposed two methods and comprehensively discuss open issues. | [
"['Feibo Jiang' 'Li Dong' 'Siwei Tu' 'Yubo Peng' 'Kezhi Wang' 'Kun Yang'\n 'Cunhua Pan' 'Dusit Niyato']"
]
|
null | null | 2404.13240 | null | null | http://arxiv.org/pdf/2404.13240v1 | 2024-04-20T02:42:46Z | 2024-04-20T02:42:46Z | Learning In Reverse Causal Strategic Environments With Ramifications on
Two Sided Markets | Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we compare employers that anticipate the strategic response of a labor force with employers that do not. We show through a combination of theory and experiment that employers with performatively optimal hiring policies improve employer reward, labor force skill level, and in some cases labor force equity. On the other hand, we demonstrate that performative employers harm labor force utility and fail to prevent discrimination in other cases. | [
"['Seamus Somerstep' 'Yuekai Sun' \"Ya'acov Ritov\"]"
]
|
null | null | 2404.13244 | null | null | http://arxiv.org/pdf/2404.13244v1 | 2024-04-20T03:05:25Z | 2024-04-20T03:05:25Z | Intelligent Agents for Auction-based Federated Learning: A Survey | Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., servers') FL training tasks. To enhance the efficiency in AFL decision support for stakeholders (i.e., data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged. However, due to the highly interdisciplinary nature of this field and the lack of a comprehensive survey providing an accessible perspective, it is a challenge for researchers to enter and contribute to this field. This paper bridges this important gap by providing a first-of-its-kind survey on the Intelligent Agents for AFL (IA-AFL) literature. We propose a unique multi-tiered taxonomy that organises existing IA-AFL works according to 1) the stakeholders served, 2) the auction mechanism adopted, and 3) the goals of the agents, to provide readers with a multi-perspective view into this field. In addition, we analyse the limitations of existing approaches, summarise the commonly adopted performance evaluation metrics, and discuss promising future directions leading towards effective and efficient stakeholder-oriented decision support in IA-AFL ecosystems. | [
"['Xiaoli Tang' 'Han Yu' 'Xiaoxiao Li' 'Sarit Kraus']"
]
|
null | null | 2404.13252 | null | null | http://arxiv.org/pdf/2404.13252v1 | 2024-04-20T03:39:54Z | 2024-04-20T03:39:54Z | 3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral
Image Classification | In recent years, Vision Transformers (ViTs) have shown promising classification performance over Convolutional Neural Networks (CNNs) due to their self-attention mechanism. Many researchers have incorporated ViTs for Hyperspectral Image (HSI) classification. HSIs are characterised by narrow contiguous spectral bands, providing rich spectral data. Although ViTs excel with sequential data, they cannot extract spectral-spatial information like CNNs. Furthermore, to have high classification performance, there should be a strong interaction between the HSI token and the class (CLS) token. To solve these issues, we propose a 3D-Convolution guided Spectral-Spatial Transformer (3D-ConvSST) for HSI classification that utilizes a 3D-Convolution Guided Residual Module (CGRM) in-between encoders to "fuse" the local spatial and spectral information and to enhance the feature propagation. Furthermore, we forego the class token and instead apply Global Average Pooling, which effectively encodes more discriminative and pertinent high-level features for classification. Extensive experiments have been conducted on three public HSI datasets to show the superiority of the proposed model over state-of-the-art traditional, convolutional, and Transformer models. The code is available at https://github.com/ShyamVarahagiri/3D-ConvSST. | [
"['Shyam Varahagiri' 'Aryaman Sinha' 'Shiv Ram Dubey' 'Satish Kumar Singh']"
]
|
null | null | 2404.13257 | null | null | http://arxiv.org/pdf/2404.13257v2 | 2024-05-18T05:10:36Z | 2024-04-20T03:57:57Z | ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow
Prediction | Traffic flow prediction, a critical aspect of intelligent transportation systems, has been increasingly popular in the field of artificial intelligence, driven by the availability of extensive traffic data. The current challenges of traffic flow prediction lie in integrating diverse factors while balancing the trade-off between computational complexity and the precision necessary for effective long-range and large-scale predictions. To address these challenges, we introduce a Spatial-Temporal Selective State Space (ST-Mamba) model, which is the first to leverage the power of spatial-temporal learning in traffic flow prediction without using graph modeling. The ST-Mamba model can effectively capture the long-range dependency for traffic flow data, thereby avoiding the issue of over-smoothing. The proposed ST-Mamba model incorporates an effective Spatial-Temporal Mixer (ST-Mixer) to seamlessly integrate spatial and temporal data processing into a unified framework and employs a Spatial-Temporal Selective State Space (ST-SSM) block to improve computational efficiency. The proposed ST-Mamba model, specifically designed for spatial-temporal data, simplifies processing procedure and enhances generalization capabilities, thereby significantly improving the accuracy of long-range traffic flow prediction. Compared to the previous state-of-the-art (SOTA) model, the proposed ST-Mamba model achieves a 61.11% improvement in computational speed and increases prediction accuracy by 0.67%. Extensive experiments with real-world traffic datasets demonstrate that the textsf{ST-Mamba} model sets a new benchmark in traffic flow prediction, achieving SOTA performance in computational efficiency for both long- and short-range predictions and significantly improving the overall efficiency and effectiveness of traffic management. | [
"['Zhiqi Shao' 'Michael G. H. Bell' 'Ze Wang' 'D. Glenn Geers' 'Haoning Xi'\n 'Junbin Gao']"
]
|
null | null | 2404.13260 | null | null | http://arxiv.org/pdf/2404.13260v1 | 2024-04-20T04:09:24Z | 2024-04-20T04:09:24Z | Predicting Diabetes with Machine Learning Analysis of Income and Health
Factors | In this study, we delve into the intricate relationships between diabetes and a range of health indicators, with a particular focus on the newly added variable of income. Utilizing data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), we analyze the impact of various factors such as blood pressure, cholesterol, BMI, smoking habits, and more on the prevalence of diabetes. Our comprehensive analysis not only investigates each factor in isolation but also explores their interdependencies and collective influence on diabetes. A novel aspect of our research is the examination of income as a determinant of diabetes risk, which to the best of our knowledge has been relatively underexplored in previous studies. We employ statistical and machine learning techniques to unravel the complex interplay between socio-economic status and diabetes, providing new insights into how financial well-being influences health outcomes. Our research reveals a discernible trend where lower income brackets are associated with a higher incidence of diabetes. In analyzing a blend of 33 variables, including health factors and lifestyle choices, we identified that features such as high blood pressure, high cholesterol, cholesterol checks, income, and Body Mass Index (BMI) are of considerable significance. These elements stand out among the myriad of factors examined, suggesting that they play a pivotal role in the prevalence and management of diabetes. | [
"['Fariba Jafari Horestani' 'M. Mehdi Owrang O']"
]
|
null | null | 2404.13265 | null | null | http://arxiv.org/pdf/2404.13265v1 | 2024-04-20T04:24:45Z | 2024-04-20T04:24:45Z | F5C-finder: An Explainable and Ensemble Biological Language Model for
Predicting 5-Formylcytidine Modifications on mRNA | As a prevalent and dynamically regulated epigenetic modification, 5-formylcytidine (f5C) is crucial in various biological processes. However, traditional experimental methods for f5C detection are often laborious and time-consuming, limiting their ability to map f5C sites across the transcriptome comprehensively. While computational approaches offer a cost-effective and high-throughput alternative, no recognition model for f5C has been developed to date. Drawing inspiration from language models in natural language processing, this study presents f5C-finder, an ensemble neural network-based model utilizing multi-head attention for the identification of f5C. Five distinct feature extraction methods were employed to construct five individual artificial neural networks, and these networks were subsequently integrated through ensemble learning to create f5C-finder. 10-fold cross-validation and independent tests demonstrate that f5C-finder achieves state-of-the-art (SOTA) performance with AUC of 0.807 and 0.827, respectively. The result highlights the effectiveness of biological language model in capturing both the order (sequential) and functional meaning (semantics) within genomes. Furthermore, the built-in interpretability allows us to understand what the model is learning, creating a bridge between identifying key sequential elements and a deeper exploration of their biological functions. | [
"['Guohao Wang' 'Ting Liu' 'Hongqiang Lyu' 'Ze Liu']"
]
|
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