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a52cb6ff-97bc-48d4-8827-e864666f5bf8 | unsupervised-learning-of-foreground-object | 1808.04593 | null | http://arxiv.org/abs/1808.04593v1 | http://arxiv.org/pdf/1808.04593v1.pdf | Unsupervised learning of foreground object detection | Unsupervised learning poses one of the most difficult challenges in computer
vision today. The task has an immense practical value with many applications in
artificial intelligence and emerging technologies, as large quantities of
unlabeled videos can be collected at relatively low cost. In this paper, we
address the unsupervised learning problem in the context of detecting the main
foreground objects in single images. We train a student deep network to predict
the output of a teacher pathway that performs unsupervised object discovery in
videos or large image collections. Our approach is different from published
methods on unsupervised object discovery. We move the unsupervised learning
phase during training time, then at test time we apply the standard
feed-forward processing along the student pathway. This strategy has the
benefit of allowing increased generalization possibilities during training,
while remaining fast at testing. Our unsupervised learning algorithm can run
over several generations of student-teacher training. Thus, a group of student
networks trained in the first generation collectively create the teacher at the
next generation. In experiments our method achieves top results on three
current datasets for object discovery in video, unsupervised image segmentation
and saliency detection. At test time the proposed system is fast, being one to
two orders of magnitude faster than published unsupervised methods. | ['Marius Leordeanu', 'Simion-Vlad Bogolin', 'Ioana Croitoru'] | 2018-08-14 | null | null | null | null | ['object-discovery-in-videos'] | ['computer-vision'] | [ 7.50106990e-01 2.64700830e-01 -2.30221942e-01 -3.26072156e-01
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e13d04a7-729f-436c-8f7e-32e143df3383 | a-study-of-the-complexity-and-accuracy-of | 1811.11787 | null | http://arxiv.org/abs/1811.11787v1 | http://arxiv.org/pdf/1811.11787v1.pdf | A Study of the Complexity and Accuracy of Direction of Arrival Estimation Methods Based on GCC-PHAT for a Pair of Close Microphones | This paper investigates the accuracy of various Generalized Cross-Correlation
with Phase Transform (GCC-PHAT) methods for a close pair of microphones. We
investigate interpolation-based methods and also propose another approach based
on Singular Value Decomposition (SVD). All investigated methods are implemented
in C code, and the execution time is measured to determine which approach is
the most appealing for real-time applications on low-cost embedded hardware. | [] | 2018-11-28 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-5.20762801e-02 -4.78097796e-01 2.75884241e-01 -5.56234606e-02
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315caf8d-bb93-44c5-a266-dff1b54b9a9d | operational-neural-networks-for-efficient | 2303.16636 | null | https://arxiv.org/abs/2303.16636v1 | https://arxiv.org/pdf/2303.16636v1.pdf | Operational Neural Networks for Efficient Hyperspectral Single-Image Super-Resolution | Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a given low-resolution input. The majority of modern super-resolution approaches use convolutional neural networks. However, convolution itself is a linear operation and the networks rely on the non-linear activation functions after each layer to provide the necessary non-linearity to learn the complex underlying function. This means that convolutional neural networks tend to be very deep to achieve the desired results. Recently, self-organized operational neural networks have been proposed that aim to overcome this limitation by replacing the convolutional filters with learnable non-linear functions through the use of MacLaurin series expansions. This work focuses on extending the convolutional filters of a popular super-resolution model to more powerful operational filters to enhance the model performance on hyperspectral images. We also investigate the effects that residual connections and different normalization types have on this type of enhanced network. Despite having fewer parameters than their convolutional network equivalents, our results show that operational neural networks achieve superior super-resolution performance on small hyperspectral image datasets. | ['Nour Aburaed', 'Mehmet Yamac', 'Serkan Kiranyaz', 'Moncef Gabbouj', 'Stephen Marshall', 'Paul Murray', 'Alexander Ulrichsen'] | 2023-03-29 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 6.67012632e-01 -3.02485079e-01 2.64473975e-01 -4.17342424e-01
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a5d4b58a-865c-41cb-9d4d-2ff28964668b | test-time-adaptation-with-perturbation | 2304.12764 | null | https://arxiv.org/abs/2304.12764v1 | https://arxiv.org/pdf/2304.12764v1.pdf | Test-Time Adaptation with Perturbation Consistency Learning | Currently, pre-trained language models (PLMs) do not cope well with the distribution shift problem, resulting in models trained on the training set failing in real test scenarios. To address this problem, the test-time adaptation (TTA) shows great potential, which updates model parameters to suit the test data at the testing time. Existing TTA methods rely on well-designed auxiliary tasks or self-training strategies based on pseudo-label. However, these methods do not achieve good trade-offs regarding performance gains and computational costs. To obtain some insights into such a dilemma, we take two representative TTA methods, i.e., Tent and OIL, for exploration and find that stable prediction is the key to achieving a good balance. Accordingly, in this paper, we propose perturbation consistency learning (PCL), a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts. Extensive experiments on adversarial robustness and cross-lingual transferring demonstrate that our method can achieve higher or comparable performance with less inference time over strong PLM backbones and previous state-of-the-art TTA methods. | ['Min Zhang', 'Hai Ye', 'Juntao Li', 'Yixin Ji', 'Yi Su'] | 2023-04-25 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [-3.30174975e-02 -3.41162123e-02 -4.02848005e-01 -3.84254575e-01
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79ed1a54-84a7-44b6-8e87-5061115e3bdc | perturbation-checklists-for-evaluating-nlg | 2109.05771 | null | https://arxiv.org/abs/2109.05771v1 | https://arxiv.org/pdf/2109.05771v1.pdf | Perturbation CheckLists for Evaluating NLG Evaluation Metrics | Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we observe that the human evaluation scores on these multiple criteria are often not correlated. For example, there is a very low correlation between human scores on fluency and data coverage for the task of structured data to text generation. This suggests that the current recipe of proposing new automatic evaluation metrics for NLG by showing that they correlate well with scores assigned by humans for a single criteria (overall quality) alone is inadequate. Indeed, our extensive study involving 25 automatic evaluation metrics across 6 different tasks and 18 different evaluation criteria shows that there is no single metric which correlates well with human scores on all desirable criteria, for most NLG tasks. Given this situation, we propose CheckLists for better design and evaluation of automatic metrics. We design templates which target a specific criteria (e.g., coverage) and perturb the output such that the quality gets affected only along this specific criteria (e.g., the coverage drops). We show that existing evaluation metrics are not robust against even such simple perturbations and disagree with scores assigned by humans to the perturbed output. The proposed templates thus allow for a fine-grained assessment of automatic evaluation metrics exposing their limitations and will facilitate better design, analysis and evaluation of such metrics. | ['Mitesh M. Khapra', 'Sreyas Mohan', 'Dev Yashpal Sheth', 'Tanay Dixit', 'Ananya B. Sai'] | 2021-09-13 | null | https://aclanthology.org/2021.emnlp-main.575 | https://aclanthology.org/2021.emnlp-main.575.pdf | emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.03489387e-01 2.62662560e-01 -7.05430866e-04 -3.67818743e-01
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c4cc76d7-d134-4b81-9c99-34420cccf76c | i3d-transformer-architectures-with-input | 2303.07624 | null | https://arxiv.org/abs/2303.07624v1 | https://arxiv.org/pdf/2303.07624v1.pdf | I3D: Transformer architectures with input-dependent dynamic depth for speech recognition | Transformer-based end-to-end speech recognition has achieved great success. However, the large footprint and computational overhead make it difficult to deploy these models in some real-world applications. Model compression techniques can reduce the model size and speed up inference, but the compressed model has a fixed architecture which might be suboptimal. We propose a novel Transformer encoder with Input-Dependent Dynamic Depth (I3D) to achieve strong performance-efficiency trade-offs. With a similar number of layers at inference time, I3D-based models outperform the vanilla Transformer and the static pruned model via iterative layer pruning. We also present interesting analysis on the gate probabilities and the input-dependency, which helps us better understand deep encoders. | ['Shinji Watanabe', 'Jaesong Lee', 'Yifan Peng'] | 2023-03-14 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.37492099e-01 6.71142340e-02 -3.28138024e-01 -5.65278172e-01
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7dc9bada-6d0d-4105-bd83-0aa09b610509 | encrypted-internet-traffic-classification | 2101.09818 | null | https://arxiv.org/abs/2101.09818v2 | https://arxiv.org/pdf/2101.09818v2.pdf | Encrypted Internet traffic classification using a supervised Spiking Neural Network | Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to enhance user experience. As internet traffic becomes increasingly encrypted, the mainstream classic traffic recognition technique, payload inspection, is rendered ineffective. This paper uses machine learning techniques for encrypted traffic classification, looking only at packet size and time of arrival. Spiking neural networks (SNN), largely inspired by how biological neurons operate, were used for two reasons. Firstly, they are able to recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware with a low energy footprint. Here we used a very simple feedforward SNN, with only one fully-connected hidden layer, and trained in a supervised manner using the newly introduced method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a very significant improvement on simplicity: input size, number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good accuracy. It turns out that, beyond spatial (i.e. packet size) features, the SNN also exploits temporal ones, mostly the nearly synchronous (within a 200ms range) arrival times of packets with certain sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low power embedded systems. | ['Timothée Masquelier', 'Saeed Bagheri Shouraki', 'Romain Zimmer', 'Carlos Aguilar-Melchor', 'Florian Delpech', 'Ali Rasteh'] | 2021-01-24 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.21422458e-01 -3.19418252e-01 -3.33556682e-01 -1.82092577e-01
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82725b0a-e95b-49a9-8ac9-34675f16e308 | fedward-flexible-federated-backdoor-defense | 2307.00356 | null | https://arxiv.org/abs/2307.00356v1 | https://arxiv.org/pdf/2307.00356v1.pdf | Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data | Federated learning (FL) enables multiple clients to collaboratively train deep learning models while considering sensitive local datasets' privacy. However, adversaries can manipulate datasets and upload models by injecting triggers for federated backdoor attacks (FBA). Existing defense strategies against FBA consider specific and limited attacker models, and a sufficient amount of noise to be injected only mitigates rather than eliminates FBA. To address these deficiencies, we introduce a Flexible Federated Backdoor Defense Framework (Fedward) to ensure the elimination of adversarial backdoors. We decompose FBA into various attacks, and design amplified magnitude sparsification (AmGrad) and adaptive OPTICS clustering (AutoOPTICS) to address each attack. Meanwhile, Fedward uses the adaptive clipping method by regarding the number of samples in the benign group as constraints on the boundary. This ensures that Fedward can maintain the performance for the Non-IID scenario. We conduct experimental evaluations over three benchmark datasets and thoroughly compare them to state-of-the-art studies. The results demonstrate the promising defense performance from Fedward, moderately improved by 33% $\sim$ 75 in clustering defense methods, and 96.98%, 90.74%, and 89.8% for Non-IID to the utmost extent for the average FBA success rate over MNIST, FMNIST, and CIFAR10, respectively. | ['Yujie Lin', 'Ximeng Liu', 'Zhiwei Zheng', 'Fuyi Wang', 'Zekai Chen'] | 2023-07-01 | null | null | null | null | ['clustering'] | ['methodology'] | [-3.02679688e-01 -2.77888209e-01 -5.40670343e-02 -2.93684453e-01
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47c67e5a-6ccf-4a66-8058-61d1a2ef4260 | ultra-large-alignments-using-phylogeny-aware | 1504.01142 | null | http://arxiv.org/abs/1504.01142v1 | http://arxiv.org/pdf/1504.01142v1.pdf | Ultra-large alignments using Phylogeny-aware Profiles | Many biological questions, including the estimation of deep evolutionary
histories and the detection of remote homology between protein sequences, rely
upon multiple sequence alignments (MSAs) and phylogenetic trees of large
datasets. However, accurate large-scale multiple sequence alignment is very
difficult, especially when the dataset contains fragmentary sequences. We
present UPP, an MSA method that uses a new machine learning technique - the
Ensemble of Hidden Markov Models - that we propose here. UPP produces highly
accurate alignments for both nucleotide and amino acid sequences, even on
ultra-large datasets or datasets containing fragmentary sequences. UPP is
available at https://github.com/smirarab/sepp. | ['Nam-phuong Nguyen', 'Keerthana Kumar', 'Tandy Warnow', 'Siavash Mirarab'] | 2015-04-05 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 4.76587921e-01 -3.28913212e-01 -3.63410115e-01 -2.30418324e-01
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1b9c80ba-131d-4b41-9952-c807af120def | single-stage-3d-geometry-preserving-depth-1 | 2306.02878 | null | https://arxiv.org/abs/2306.02878v1 | https://arxiv.org/pdf/2306.02878v1.pdf | Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data | Nowadays, robotics, AR, and 3D modeling applications attract considerable attention to single-view depth estimation (SVDE) as it allows estimating scene geometry from a single RGB image. Recent works have demonstrated that the accuracy of an SVDE method hugely depends on the diversity and volume of the training data. However, RGB-D datasets obtained via depth capturing or 3D reconstruction are typically small, synthetic datasets are not photorealistic enough, and all these datasets lack diversity. The large-scale and diverse data can be sourced from stereo images or stereo videos from the web. Typically being uncalibrated, stereo data provides disparities up to unknown shift (geometrically incomplete data), so stereo-trained SVDE methods cannot recover 3D geometry. It was recently shown that the distorted point clouds obtained with a stereo-trained SVDE method can be corrected with additional point cloud modules (PCM) separately trained on the geometrically complete data. On the contrary, we propose GP$^{2}$, General-Purpose and Geometry-Preserving training scheme, and show that conventional SVDE models can learn correct shifts themselves without any post-processing, benefiting from using stereo data even in the geometry-preserving setting. Through experiments on different dataset mixtures, we prove that GP$^{2}$-trained models outperform methods relying on PCM in both accuracy and speed, and report the state-of-the-art results in the general-purpose geometry-preserving SVDE. Moreover, we show that SVDE models can learn to predict geometrically correct depth even when geometrically complete data comprises the minor part of the training set. | ['Anton Konushin', 'Mikhail Artemyev', 'Anna Vorontsova', 'Mikhail Romanov', 'Nikolay Patakin'] | 2023-06-05 | single-stage-3d-geometry-preserving-depth | http://openaccess.thecvf.com//content/CVPR2022/html/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 2.38618627e-01 8.27815905e-02 2.75193751e-01 -2.06183881e-01
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39869557-07f4-4fc2-a3d7-641f56c18828 | 171010324 | 1710.10324 | null | http://arxiv.org/abs/1710.10324v3 | http://arxiv.org/pdf/1710.10324v3.pdf | Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties | The use of machine learning methods for accelerating the design of
crystalline materials usually requires manually constructed feature vectors or
complex transformation of atom coordinates to input the crystal structure,
which either constrains the model to certain crystal types or makes it
difficult to provide chemical insights. Here, we develop a crystal graph
convolutional neural networks framework to directly learn material properties
from the connection of atoms in the crystal, providing a universal and
interpretable representation of crystalline materials. Our method provides a
highly accurate prediction of density functional theory calculated properties
for eight different properties of crystals with various structure types and
compositions after being trained with $10^4$ data points. Further, our
framework is interpretable because one can extract the contributions from local
chemical environments to global properties. Using an example of perovskites, we
show how this information can be utilized to discover empirical rules for
materials design. | ['Tian Xie', 'Jeffrey C. Grossman'] | 2017-10-27 | crystal-graph-convolutional-neural-networks | null | null | phys-rev-lett-2017-10 | ['formation-energy'] | ['miscellaneous'] | [ 9.69033688e-02 1.21897804e-02 -5.34011841e-01 -4.79691088e-01
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b6ae70c2-1dd0-4dc2-b2d4-b1b66c0cf59d | knowledge-based-conversational-search | 1912.06859 | null | https://arxiv.org/abs/1912.06859v1 | https://arxiv.org/pdf/1912.06859v1.pdf | Knowledge-based Conversational Search | Conversational interfaces that allow for intuitive and comprehensive access to digitally stored information remain an ambitious goal. In this thesis, we lay foundations for designing conversational search systems by analyzing the requirements and proposing concrete solutions for automating some of the basic components and tasks that such systems should support. We describe several interdependent studies that were conducted to analyse the design requirements for more advanced conversational search systems able to support complex human-like dialogue interactions and provide access to vast knowledge repositories. In the first two research chapters, we focus on analyzing the structures common to information-seeking dialogues by capturing recurrent patterns in terms of both domain-independent functional relations between utterances as well as domain-specific implicit semantic relations from shared background knowledge. Our results show that question answering is one of the key components required for efficient information access but it is not the only type of dialogue interactions that a conversational search system should support. In the third research chapter, we propose a novel approach for complex question answering from a knowledge graph that surpasses the current state-of-the-art results in terms of both efficacy and efficiency. In the last research chapter, we turn our attention towards an alternative interaction mode, which we termed conversational browsing, in which, unlike question answering, the conversational system plays a more pro-active role in the course of a dialogue interaction. We show that this approach helps users to discover relevant items that are difficult to retrieve using only question answering due to the vocabulary mismatch problem. | ['Svitlana Vakulenko'] | 2019-12-14 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.69010296e-01 5.24873972e-01 1.11733072e-01 -2.42623597e-01
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84363d50-cdea-4786-8a0f-c5e20bb659aa | does-vision-accelerate-hierarchical | 2302.00667 | null | https://arxiv.org/abs/2302.00667v1 | https://arxiv.org/pdf/2302.00667v1.pdf | Does Vision Accelerate Hierarchical Generalization of Neural Language Learners? | Neural language models (LMs) are arguably less data-efficient than humans -- why does this gap occur? In this study, we hypothesize that this gap stems from the learners' accessibility to modalities other than text, specifically, vision. We conducted two complementary experiments (using noisy, realistic data and a simplified, artificial one) toward the advantage of vision in the syntactic generalization of LMs. Our results showed that vision accelerated a proper linguistic generalization in the simplified, artificial setting, but LMs struggled with the noisy, realistic setting. These mixed results indicate several possibilities, e.g., vision can potentially boost language acquisition, but learners' additional visual/linguistic prior knowledge should be needed to robustly make use of raw images for efficient language acquisition. | ['Tatsuki Kuribayashi'] | 2023-02-01 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-8.93595815e-02 9.57206562e-02 1.21155344e-01 -2.19755813e-01
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737e9f0f-25fa-47a5-b91d-90aa095f6502 | enhanced-distribution-modelling-via-augmented | 2306.02731 | null | https://arxiv.org/abs/2306.02731v1 | https://arxiv.org/pdf/2306.02731v1.pdf | Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows | While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transformation underlying neural ODEs has been shown to be suboptimal. In this paper, we present AFFJORD, a neural ODE-based normalizing flow which enhances the representation power of FFJORD by defining the neural ODE through special augmented transformation dynamics which preserve the topology of the space. Furthermore, we derive the Jacobian determinant of the general augmented form by generalizing the chain rule in the continuous sense into the Cable Rule, which expresses the forward sensitivity of ODEs with respect to their initial conditions. The cable rule gives an explicit expression for the Jacobian of a neural ODE transformation, and provides an elegant proof of the instantaneous change of variable. Our experimental results on density estimation in synthetic and high dimensional data, such as MNIST, CIFAR-10 and CelebA 32x32, show that AFFJORD outperforms the baseline FFJORD through the improved flexibility of the underlying vector field. | ['Marco Lorenzi', 'Etrit Haxholli'] | 2023-06-05 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-4.40837324e-01 7.61561692e-02 1.78940073e-01 -2.39157006e-01
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7a267f7f-affe-490e-ae08-f3639ff57f35 | simple-text-detoxification-by-identifying-a | 2112.08346 | null | https://arxiv.org/abs/2112.08346v1 | https://arxiv.org/pdf/2112.08346v1.pdf | Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings | Large pre-trained language models are often trained on large volumes of internet data, some of which may contain toxic or abusive language. Consequently, language models encode toxic information, which makes the real-world usage of these language models limited. Current methods aim to prevent toxic features from appearing generated text. We hypothesize the existence of a low-dimensional toxic subspace in the latent space of pre-trained language models, the existence of which suggests that toxic features follow some underlying pattern and are thus removable. To construct this toxic subspace, we propose a method to generalize toxic directions in the latent space. We also provide a methodology for constructing parallel datasets using a context based word masking system. Through our experiments, we show that when the toxic subspace is removed from a set of sentence representations, almost no toxic representations remain in the result. We demonstrate empirically that the subspace found using our method generalizes to multiple toxicity corpora, indicating the existence of a low-dimensional toxic subspace. | ['Yangfeng Ji', 'Mohit Sudhakar', 'Andrew Wang'] | 2021-12-15 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [ 2.37970129e-01 -1.48306310e-01 -1.49452522e-01 7.92071372e-02
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4fda3822-d63e-4561-8327-892ecc3259a1 | grounded-language-learning-from-video | null | null | https://aclanthology.org/P13-1006 | https://aclanthology.org/P13-1006.pdf | Grounded Language Learning from Video Described with Sentences | null | ['Haonan Yu', 'Jeffrey Mark Siskind'] | 2013-08-01 | null | null | null | acl-2013-8 | ['grounded-language-learning'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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07a59574-6c2c-4196-85b8-9561f3731f82 | figaro-generating-symbolic-music-with-fine | 2201.10936 | null | https://arxiv.org/abs/2201.10936v3 | https://arxiv.org/pdf/2201.10936v3.pdf | FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control | Generating music with deep neural networks has been an area of active research in recent years. While the quality of generated samples has been steadily increasing, most methods are only able to exert minimal control over the generated sequence, if any. We propose the self-supervised description-to-sequence task, which allows for fine-grained controllable generation on a global level. We do so by extracting high-level features about the target sequence and learning the conditional distribution of sequences given the corresponding high-level description in a sequence-to-sequence modelling setup. We train FIGARO (FIne-grained music Generation via Attention-based, RObust control) by applying description-to-sequence modelling to symbolic music. By combining learned high level features with domain knowledge, which acts as a strong inductive bias, the model achieves state-of-the-art results in controllable symbolic music generation and generalizes well beyond the training distribution. | ['Thomas Hofmann', 'Yannic Kilcher', 'Luca Biggio', 'Dimitri von Rütte'] | 2022-01-26 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 6.22220039e-01 1.63961411e-01 -3.13911945e-01 -7.32591897e-02
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9894312d-1d1c-4d31-a23d-cd7be6223b9d | counting-to-explore-and-generalize-in-text | 1806.11525 | null | http://arxiv.org/abs/1806.11525v2 | http://arxiv.org/pdf/1806.11525v2.pdf | Counting to Explore and Generalize in Text-based Games | We propose a recurrent RL agent with an episodic exploration mechanism that
helps discovering good policies in text-based game environments. We show
promising results on a set of generated text-based games of varying difficulty
where the goal is to collect a coin located at the end of a chain of rooms. In
contrast to previous text-based RL approaches, we observe that our agent learns
policies that generalize to unseen games of greater difficulty. | ['Marc-Alexandre Côté', 'Xingdi Yuan', 'Alessandro Sordoni', 'Romain Laroche', 'Remi Tachet des Combes', 'Adam Trischler', 'Matthew Hausknecht'] | 2018-06-29 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-2.11110443e-01 4.31442976e-01 -2.86622234e-02 2.38896698e-01
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c185bd7a-cd30-4970-97ff-847236b51747 | ltrc-mup-2022-multi-perspective-scientific | null | null | https://aclanthology.org/2022.sdp-1.35 | https://aclanthology.org/2022.sdp-1.35.pdf | LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models | The MuP-2022 shared task focuses on multiperspective scientific document summarization. Given a scientific document, with multiple reference summaries, our goal was to develop a model that can produce a generic summary covering as many aspects of the document as covered by all of its reference summaries. This paper describes our best official model, a finetuned BART-large, along with a discussion on the challenges of this task and some of our unofficial models including SOTA generation models. Our submitted model out performedthe given, MuP 2022 shared task, baselines on ROUGE-2, ROUGE-L and average ROUGE F1-scores. Code of our submission can be ac- cessed here. | ['Manish Shrivastava', 'Nirmal Surange', 'Ashok Urlana'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.03750452e-01 3.83380592e-01 -3.69030088e-01 -7.74634955e-03
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f00675fc-3a91-4f56-b669-959030e6a7ce | mastering-nordschleife-a-comprehensive-race | 2306.16088 | null | https://arxiv.org/abs/2306.16088v1 | https://arxiv.org/pdf/2306.16088v1.pdf | Mastering Nordschleife -- A comprehensive race simulation for AI strategy decision-making in motorsports | In the realm of circuit motorsports, race strategy plays a pivotal role in determining race outcomes. This strategy focuses on the timing of pit stops, which are necessary due to fuel consumption and tire performance degradation. The objective of race strategy is to balance the advantages of pit stops, such as tire replacement and refueling, with the time loss incurred in the pit lane. Current race simulations, used to estimate the best possible race strategy, vary in granularity, modeling of probabilistic events, and require manual input for in-laps. This paper addresses these limitations by developing a novel simulation model tailored to GT racing and leveraging artificial intelligence to automate strategic decisions. By integrating the simulation with OpenAI's Gym framework, a reinforcement learning environment is created and an agent is trained. The study evaluates various hyperparameter configurations, observation spaces, and reward functions, drawing upon historical timing data from the 2020 N\"urburgring Langstrecken Serie for empirical parameter validation. The results demonstrate the potential of reinforcement learning for improving race strategy decision-making, as the trained agent makes sensible decisions regarding pit stop timing and refueling amounts. Key parameters, such as learning rate, decay rate and the number of episodes, are identified as crucial factors, while the combination of fuel mass and current race position proves most effective for policy development. The paper contributes to the broader application of reinforcement learning in race simulations and unlocks the potential for race strategy optimization beyond FIA Formula~1, specifically in the GT racing domain. | ['David Klotz', 'Max Boettinger'] | 2023-06-28 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-5.91484345e-02 -9.45451483e-02 -7.08341718e-01 8.34789574e-02
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ef15bf62-59ef-4d96-a163-a46386e2c343 | deep-traffic-sign-detection-and-recognition | 2008.00962 | null | https://arxiv.org/abs/2008.00962v1 | https://arxiv.org/pdf/2008.00962v1.pdf | Deep Traffic Sign Detection and Recognition Without Target Domain Real Images | Deep learning has been successfully applied to several problems related to autonomous driving, often relying on large databases of real target-domain images for proper training. The acquisition of such real-world data is not always possible in the self-driving context, and sometimes their annotation is not feasible. Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. Particularly, traffic sign detection is a challenging problem in which these three issues are seen altogether. To address these challenges, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. The effortlessly generated database is shown to be effective for the training of a deep detector on traffic signs from multiple countries. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one. When compared to training with a smaller data set of real images, training with synthetic images increased the accuracy by 12.25%. The proposed method also improves the performance of the detector when target-domain data are available. | ['Thiago Oliveira-Santos', 'Claudine Badue', 'Alberto F. de Souza', 'Thiago M. Paixão', 'Lucas Tabelini', 'Rodrigo Berriel', 'Nicu Sebe'] | 2020-07-30 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.55431086e-01 -5.40259629e-02 2.84226704e-02 -3.48662049e-01
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1be93c7b-d2bc-457d-842d-ed053e55ec30 | migrant-laborer-s-optimization-mechanism | 2306.08829 | null | https://arxiv.org/abs/2306.08829v1 | https://arxiv.org/pdf/2306.08829v1.pdf | Migrant Laborer's Optimization Mechanism Under Employment Permit System(EPS): Introducing and Analyzing 'Skill-Relevance-Self Selection' Model | Migrant laborers subject to ROK's Employment Permit System(EPS) must strike a balance between host country's high wage and 'Depreciation of skill-relevance entailed by immigration', whilst taking account of the 'migration costs'. This study modelizes the optimization mechanism of migrant workers and the firms hiring them -- then induces the solution of the very model, namely, 'Subgame Perfect Nash Equilibrium(SPNE)', by utilizing game theory's 'backward induction' method. Analyzing the dynamics between variables at SPNE state, the attained stylized facts are what as follows; [1]Host nation's skill-relevance and wage differential have positive correlation. [2]Emigrating nation's skill-relevance and wage differential have negative correlation. Both stylized facts -- [1,2] -- are operationalized into 'Host nation skill-relevance hypothesis(H1)' and 'Emigrating nation skill-relevance hypothesis(H2)', respectively; of which are thoroughly tested by OLS linear regression analysis. In all sex/gender parameters(Total/Men/Women), test results support both hypotheses with statistical significance, thereby inductively substantiating the constructed model. This paper contributes to existing labor immigration literature in three following aspects: (1)Stimulate the economic approach to migrant labor analysis, and by such means, break away from the overflow of sociology, anthropology, political science, and jurisprudence in prior studies; (2)Shed a light on the EPS's microeconomic interaction process, of which was left undisclosed as a 'black box'; (3)Seek a complementary synthesis of two grand strands of research methodology -- that is, deductive modeling and inductive statistics. | ['Sunghyun Cho', 'Yejin Lim', 'Kwonhyung Lee'] | 2023-06-15 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [-2.67161757e-01 3.26073229e-01 -1.02789009e+00 3.84060532e-01
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0592ba6f-47e0-4c57-99fa-624bba8e73f8 | altitude-loss-optimal-glides-in-engine | 2304.06499 | null | https://arxiv.org/abs/2304.06499v1 | https://arxiv.org/pdf/2304.06499v1.pdf | Altitude-Loss Optimal Glides in Engine Failure Emergencies -- Accounting for Ground Obstacles and Wind | Engine failure is a recurring emergency in General Aviation and fixed-wing UAVs, often requiring the pilot or remote operator to carry out carefully planned glides to safely reach a candidate landing strip. We tackle the problem of minimizing the altitude loss of a thrustless aircraft flying towards a designated target position. Extending previous work on optimal glides without obstacles, we consider here trajectory planning of optimal gliding in the the presence of ground obstacles, while accounting for wind effects. Under simplifying model assumptions, in particular neglecting the effect of turns, we characterize the optimal solution as comprising straight glide segments between iteratively-determined extreme points on the obstacles. Consequently, the optimal trajectory is included in an iteratively-defined reduced visibility graph, and can be obtained by a standard graph search algorithm, such as A$^*$. We further quantify the effect of turns to verify a safe near-optimal glide trajectory. We apply our algorithm on a Cessna 172 model, in realistic scenarios, demonstrating both the altitude-loss optimal trajectory calculation, and determination of airstrip reachability. | ['Nahum Shimkin', 'Aharon Bar-Gill', 'Daniel Segal'] | 2023-04-13 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 1.56624869e-01 4.32930402e-02 2.91208088e-01 2.38439903e-01
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4a687c40-5304-45cf-84c2-8894f47bc8ff | an-effective-system-for-multi-format | 2108.06957 | null | https://arxiv.org/abs/2108.06957v1 | https://arxiv.org/pdf/2108.06957v1.pdf | An Effective System for Multi-format Information Extraction | The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-level and document-level. Here we describe our system for this multi-format information extraction competition task. Specifically, for the relation extraction subtask, we convert it to a traditional triple extraction task and design a voting based method that makes full use of existing models. For the sentence-level event extraction subtask, we convert it to a NER task and use a pointer labeling based method for extraction. Furthermore, considering the annotated trigger information may be helpful for event extraction, we design an auxiliary trigger recognition model and use the multi-task learning mechanism to integrate the trigger features into the event extraction model. For the document-level event extraction subtask, we design an Encoder-Decoder based method and propose a Transformer-alike decoder. Finally,our system ranks No.4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79.887%, 85.179%, and 70.828% respectively. The codes of our model are available at {https://github.com/neukg/MultiIE}. | ['Feiliang Ren', 'Xiaofeng Zhao', 'Shujuan Yin', 'Longhui Zhang', 'Yaduo Liu'] | 2021-08-16 | null | null | null | null | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 1.19505696e-01 4.00824934e-01 -3.54727864e-01 -2.78889745e-01
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1fc1c185-54a0-432c-abad-a5585a55988e | system-status-aware-adaptive-network-for | 2303.15742 | null | https://arxiv.org/abs/2303.15742v2 | https://arxiv.org/pdf/2303.15742v2.pdf | System-status-aware Adaptive Network for Online Streaming Video Understanding | Recent years have witnessed great progress in deep neural networks for real-time applications. However, most existing works do not explicitly consider the general case where the device's state and the available resources fluctuate over time, and none of them investigate or address the impact of varying computational resources for online video understanding tasks. This paper proposes a System-status-aware Adaptive Network (SAN) that considers the device's real-time state to provide high-quality predictions with low delay. Usage of our agent's policy improves efficiency and robustness to fluctuations of the system status. On two widely used video understanding tasks, SAN obtains state-of-the-art performance while constantly keeping processing delays low. Moreover, training such an agent on various types of hardware configurations is not easy as the labeled training data might not be available, or can be computationally prohibitive. To address this challenging problem, we propose a Meta Self-supervised Adaptation (MSA) method that adapts the agent's policy to new hardware configurations at test-time, allowing for easy deployment of the model onto other unseen hardware platforms. | ['Jun Liu', 'Zhipeng Fan', 'Jia Gong', 'Lin Geng Foo'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [-5.69357611e-02 -3.07835519e-01 -4.65284616e-01 -4.44489419e-01
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446f0ae8-f63d-408f-9eaf-9df8a3bedd92 | pre-clustering-point-clouds-of-crop-fields | 2107.10950 | null | https://arxiv.org/abs/2107.10950v2 | https://arxiv.org/pdf/2107.10950v2.pdf | Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods | In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we notice a similarity between the current state-of-the-art for separating corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a number of novel, application-specific algorithms with the goal of producing a general and scalable field segmentation algorithm. The novel algorithms proposed in this work are shown to produce quantitatively better results than the current state-of-the-art while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithms should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished. | ['Nikolaos Papanikolopoulos', 'Henry J. Nelson'] | 2021-07-22 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.73242378e-01 4.72599454e-02 -2.40972579e-01 -2.05494389e-01
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bbc1766a-712e-415d-87b8-75497ca43c56 | are-large-language-models-good-evaluators-for | 2305.13091 | null | https://arxiv.org/abs/2305.13091v1 | https://arxiv.org/pdf/2305.13091v1.pdf | Are Large Language Models Good Evaluators for Abstractive Summarization? | Human evaluations are often required for abstractive summary evaluations to give fairer judgments. However, they are often time-consuming, costly, inconsistent, and non-reproducible. To overcome these challenges, we explore the potential of using an out-of-the-box LLM (i.e. "gpt-3.5-turbo") for summarization evaluation without manually selecting demonstrations or complex prompt tuning. We compare different evaluation methods, including 2 methods for Likert-scale scoring and 1 method for head-to-head comparisons, to investigate the performance of the LLM as a zero-shot evaluator. We further propose a meta-correlation metric to measure the stability of the LLM's evaluation capability. With extensive experiments, we show that certain prompt formats can produce better results than others. We also bring attention to the LLM's deteriorating evaluation capability with the rising qualities of summaries. In addition, we find that the LLM's evaluation capability also depends on the evaluated dimensions. We discuss the pros and cons of each method, make recommendations, and suggest some future directions for improvement. | ['Lidong Bing', 'Yang You', 'Liying Cheng', 'Chenhui Shen'] | 2023-05-22 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [-2.65100598e-01 -2.31712341e-01 -3.54791939e-01 -5.26225626e-01
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2e5a2911-2286-4c27-b6c9-f3be497439fb | lgpma-complicated-table-structure-recognition | 2105.06224 | null | https://arxiv.org/abs/2105.06224v3 | https://arxiv.org/pdf/2105.06224v3.pdf | LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment | Table structure recognition is a challenging task due to the various structures and complicated cell spanning relations. Previous methods handled the problem starting from elements in different granularities (rows/columns, text regions), which somehow fell into the issues like lossy heuristic rules or neglect of empty cell division. Based on table structure characteristics, we find that obtaining the aligned bounding boxes of text region can effectively maintain the entire relevant range of different cells. However, the aligned bounding boxes are hard to be accurately predicted due to the visual ambiguities. In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features. Specifically, we propose the framework of Local and Global Pyramid Mask Alignment, which adopts the soft pyramid mask learning mechanism in both the local and global feature maps. It allows the predicted boundaries of bounding boxes to break through the limitation of original proposals. A pyramid mask re-scoring module is then integrated to compromise the local and global information and refine the predicted boundaries. Finally, we propose a robust table structure recovery pipeline to obtain the final structure, in which we also effectively solve the problems of empty cells locating and division. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on several public benchmarks. | ['Fei Wu', 'Wenming Tan', 'Wenqi Ren', 'Yi Niu', 'ShiLiang Pu', 'Peng Zhang', 'Zhanzhan Cheng', 'Zaisheng Li', 'Liang Qiao'] | 2021-05-13 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 5.36436252e-02 -3.00701290e-01 -4.49979812e-01 -1.04747415e-01
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cd0b632a-d5b5-4adc-aed2-a87f78378d55 | residual-attention-based-network-for | 2107.08425 | null | https://arxiv.org/abs/2107.08425v1 | https://arxiv.org/pdf/2107.08425v1.pdf | Residual Attention Based Network for Automatic Classification of Phonation Modes | Phonation mode is an essential characteristic of singing style as well as an important expression of performance. It can be classified into four categories, called neutral, breathy, pressed and flow. Previous studies used voice quality features and feature engineering for classification. While deep learning has achieved significant progress in other fields of music information retrieval (MIR), there are few attempts in the classification of phonation modes. In this study, a Residual Attention based network is proposed for automatic classification of phonation modes. The network consists of a convolutional network performing feature processing and a soft mask branch enabling the network focus on a specific area. In comparison experiments, the models with proposed network achieve better results in three of the four datasets than previous works, among which the highest classification accuracy is 94.58%, 2.29% higher than the baseline. | ['Wei Li', 'Yiliang Jiang', 'Xiaoheng Sun'] | 2021-07-18 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-7.87499398e-02 -5.43972194e-01 -2.06211403e-01 1.18032888e-01
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a3ec2b38-3bc8-46be-bda1-2748f5e90b8e | mental-arithmetic-task-classification-with | 2209.11767 | null | https://arxiv.org/abs/2209.11767v2 | https://arxiv.org/pdf/2209.11767v2.pdf | Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG | In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method. | ['Stephane Perrey', 'Jacky Montmain', 'Gérard Dray', 'Binbin Xu', 'Zaineb Ajra'] | 2022-09-26 | null | null | null | null | ['mental-arithmetic-task'] | ['medical'] | [-1.48055609e-02 -1.61715850e-01 3.28150779e-01 -5.69212735e-02
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09478734-98e9-49e9-a9d5-8555b06a1356 | document-level-event-factuality-1 | null | null | https://aclanthology.org/2022.coling-1.231 | https://aclanthology.org/2022.coling-1.231.pdf | Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning | Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP). However, most previous studies only considered sentence-level task and did not adopt document-level knowledge. Moreover, they modelled DEFI as a typical text classification task depending on annotated information heavily, and limited to the task-specific corpus only, which resulted in data scarcity. To tackle these issues, we propose a new framework formulating DEFI as Machine Reading Comprehension (MRC) tasks considering both Span-Extraction (Ext) and Multiple-Choice (Mch). Our model does not employ any other explicit annotated information, and utilizes Transfer Learning (TL) to extract knowledge from universal large-scale MRC corpora for cross-domain data augmentation. The empirical results on DLEFM corpus demonstrate that the proposed model outperforms several state-of-the-arts. | ['Guodong Zhou', 'Qiaoming Zhu', 'Peifeng Li', 'Heng Zhang', 'Zhong Qian'] | null | null | null | null | coling-2022-10 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.79363412e-01 1.43837348e-01 -3.39797318e-01 -2.79871851e-01
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15294011-f762-4518-bc1c-9d965a4a2501 | musika-fast-infinite-waveform-music | 2208.08706 | null | https://arxiv.org/abs/2208.08706v1 | https://arxiv.org/pdf/2208.08706v1.pdf | Musika! Fast Infinite Waveform Music Generation | Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use. In this work, we introduce Musika, a music generation system that can be trained on hundreds of hours of music using a single consumer GPU, and that allows for much faster than real-time generation of music of arbitrary length on a consumer CPU. We achieve this by first learning a compact invertible representation of spectrogram magnitudes and phases with adversarial autoencoders, then training a Generative Adversarial Network (GAN) on this representation for a particular music domain. A latent coordinate system enables generating arbitrarily long sequences of excerpts in parallel, while a global context vector allows the music to remain stylistically coherent through time. We perform quantitative evaluations to assess the quality of the generated samples and showcase options for user control in piano and techno music generation. We release the source code and pretrained autoencoder weights at github.com/marcoppasini/musika, such that a GAN can be trained on a new music domain with a single GPU in a matter of hours. | ['Jan Schlüter', 'Marco Pasini'] | 2022-08-18 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.43688449e-01 -7.52538219e-02 2.05418125e-01 2.77830154e-01
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8ec02935-a7fa-481c-994d-923d99726dea | gh-feat-learning-versatile-generative | 2301.05315 | null | https://arxiv.org/abs/2301.05315v1 | https://arxiv.org/pdf/2301.05315v1.pdf | GH-Feat: Learning Versatile Generative Hierarchical Features from GANs | Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones. We first train an encoder by considering the pretrained StyleGAN generator as a learned loss function. The visual features produced by our encoder, termed as Generative Hierarchical Features (GH-Feat), highly align with the layer-wise GAN representations, and hence describe the input image adequately from the reconstruction perspective. Extensive experiments support the versatile transferability of GH-Feat across a range of applications, such as image editing, image processing, image harmonization, face verification, landmark detection, layout prediction, image retrieval, etc. We further show that, through a proper spatial expansion, our developed GH-Feat can also facilitate fine-grained semantic segmentation using only a few annotations. Both qualitative and quantitative results demonstrate the appealing performance of GH-Feat. | ['Bolei Zhou', 'Ceyuan Yang', 'Jiapeng Zhu', 'Yujun Shen', 'Yinghao Xu'] | 2023-01-12 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 5.61988592e-01 3.90176088e-01 1.41792325e-02 -4.91993457e-01
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64874b0f-5f70-4b6d-b2ca-adb6dae7cbae | rcurrency-live-digital-asset-trading-using-a | 2106.06972 | null | https://arxiv.org/abs/2106.06972v1 | https://arxiv.org/pdf/2106.06972v1.pdf | RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural Network-based Forecasting System | Consistent alpha generation, i.e., maintaining an edge over the market, underpins the ability of asset traders to reliably generate profits. Technical indicators and trading strategies are commonly used tools to determine when to buy/hold/sell assets, yet these are limited by the fact that they operate on known values. Over the past decades, multiple studies have investigated the potential of artificial intelligence in stock trading in conventional markets, with some success. In this paper, we present RCURRENCY, an RNN-based trading engine to predict data in the highly volatile digital asset market which is able to successfully manage an asset portfolio in a live environment. By combining asset value prediction and conventional trading tools, RCURRENCY determines whether to buy, hold or sell digital currencies at a given point in time. Experimental results show that, given the data of an interval $t$, a prediction with an error of less than 0.5\% of the data at the subsequent interval $t+1$ can be obtained. Evaluation of the system through backtesting shows that RCURRENCY can be used to successfully not only maintain a stable portfolio of digital assets in a simulated live environment using real historical trading data but even increase the portfolio value over time. | ['Jan S. Rellermeyer', 'Hugo Kooijman', 'Ashay Somai', 'Ralph van Gurp', 'Yapeng Jasper Hu'] | 2021-06-13 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-4.39080179e-01 -3.51029903e-01 -6.26493618e-02 4.91934009e-02
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a094f0dc-1b93-4759-8e9d-de62d1a9cede | hypernetworks-for-zero-shot-transfer-in | 2211.15457 | null | https://arxiv.org/abs/2211.15457v2 | https://arxiv.org/pdf/2211.15457v2.pdf | Hypernetworks for Zero-shot Transfer in Reinforcement Learning | In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches. | ['David Meger', 'Gregory Dudek', 'Francois Robert Hogan', 'Charlotte Morissette', 'Sahand Rezaei-Shoshtari'] | 2022-11-28 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.73700655e-01 4.04055297e-01 -4.42342579e-01 -6.81709349e-02
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f29df373-5ba0-4d83-9a97-46e6c270be79 | alignerf-high-fidelity-neural-radiance-fields | 2211.09682 | null | https://arxiv.org/abs/2211.09682v1 | https://arxiv.org/pdf/2211.09682v1.pdf | AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training | Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent effects, few efforts have been devoted to exploring its limits in a high-resolution setting. Specifically, existing NeRF-based methods face several limitations when reconstructing high-resolution real scenes, including a very large number of parameters, misaligned input data, and overly smooth details. In this work, we conduct the first pilot study on training NeRF with high-resolution data and propose the corresponding solutions: 1) marrying the multilayer perceptron (MLP) with convolutional layers which can encode more neighborhood information while reducing the total number of parameters; 2) a novel training strategy to address misalignment caused by moving objects or small camera calibration errors; and 3) a high-frequency aware loss. Our approach is nearly free without introducing obvious training/testing costs, while experiments on different datasets demonstrate that it can recover more high-frequency details compared with the current state-of-the-art NeRF models. Project page: \url{https://yifanjiang.net/alignerf.} | ['Tianfan Xue', 'Zhangyang Wang', 'Jonathan T. Barron', 'Dejia Xu', 'Ben Mildenhall', 'Peter Hedman', 'Yifan Jiang'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration'] | ['computer-vision'] | [ 3.11429858e-01 -2.51946837e-01 2.81617671e-01 -4.89825755e-01
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3853a2fe-2dd5-494a-b352-ecbf4103c2e1 | deep-data-flow-analysis-1 | 2012.01470 | null | https://arxiv.org/abs/2012.01470v1 | https://arxiv.org/pdf/2012.01470v1.pdf | Deep Data Flow Analysis | Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture, and other optimizations, is alluring. However, most machine learning methods cannot replicate even the simplest of the abstract interpretations of data flow analysis that are critical to making good optimization decisions. This must change for machine learning to become the dominant technology in compiler heuristics. To this end, we propose ProGraML - Program Graphs for Machine Learning - a language-independent, portable representation of whole-program semantics for deep learning. To benchmark current and future learning techniques for compiler analyses we introduce an open dataset of 461k Intermediate Representation (IR) files for LLVM, covering five source programming languages, and 15.4M corresponding data flow results. We formulate data flow analysis as an MPNN and show that, using ProGraML, standard analyses can be learned, yielding improved performance on downstream compiler optimization tasks. | ["Michael O'Boyle", 'Torsten Hoefler', 'Tal Ben-Nun', 'Zacharias Fisches', 'Hugh Leather', 'Chris Cummins'] | 2020-11-21 | deep-data-flow-analysis | https://openreview.net/forum?id=SPhswbiXpJQ | https://openreview.net/pdf?id=SPhswbiXpJQ | null | ['compiler-optimization'] | ['computer-code'] | [-1.12643346e-01 1.49964206e-02 -1.12955344e+00 -6.13623381e-01
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0fd50d3c-59b8-4fea-8b2d-077a9444aa74 | squarified-treemaps | null | null | https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6685 | https://www.win.tue.nl/~vanwijk/stm.pdf | Squarified Treemaps | An extension to the treemap method for the visualization of hierarchical information, such as directory structures and organization structures, is presented. The standard treemap method often gives thin, elongated rectangles. As a result, rectangles are difficult to compare and to select. A new method is presented to generate lay-outs in which the rectangles approximate squares. To strenghten the visualization of the structure, shaded frames are used around groups of related nodes. | ['Jarke J. van Wijk', 'Kees Huizing', 'Mark Bruls'] | 1999-10-01 | null | null | null | ieee-tcvg-symposium-on-visualization-1999-10 | ['tree-map-layout'] | ['graphs'] | [-3.29121679e-01 2.79554874e-01 1.37418762e-01 -2.77489573e-01
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3384146f-3ccb-4e34-8b7f-f41f226ecdb0 | wildfire-forecasting-with-satellite-images | 2208.09411 | null | https://arxiv.org/abs/2208.09411v2 | https://arxiv.org/pdf/2208.09411v2.pdf | Wildfire Forecasting with Satellite Images and Deep Generative Model | Wildfire forecasting has been one of the most critical tasks that humanities want to thrive. It plays a vital role in protecting human life. Wildfire prediction, on the other hand, is difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images as a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts is based on stochastic image-autoregressive recurrent networks, which raises various performance and application difficulties, such as computational cost and limited efficiency on massive datasets. Another possibility is to use entirely latent temporal models that combine frame synthesis and temporal dynamics. However, due to design and training issues, no such model for stochastic video prediction has yet been proposed in the literature. This paper addresses these issues by introducing a novel stochastic temporal model whose dynamics are driven in a latent space. It naturally predicts video dynamics by allowing our lighter, more interpretable latent model to beat previous state-of-the-art approaches on the GOES-16 dataset. Results will be compared towards various benchmarking models. | ['Chris Schmidt', 'Sang Truong', 'Thai-Nam Hoang'] | 2022-08-19 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.14273566e-01 -2.64403135e-01 -9.11055040e-03 -1.78864107e-01
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36c2a4ed-84df-4377-91de-bde7e151c32d | the-second-place-solution-for-the-4th-large | 2206.12035 | null | https://arxiv.org/abs/2206.12035v1 | https://arxiv.org/pdf/2206.12035v1.pdf | The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation | The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames. Due to the requirement of understanding cross-modal semantics within individual instances, this task is more challenging than the traditional semi-supervised video object segmentation where the ground truth object masks in the first frame are given. With the great achievement of Transformer in object detection and object segmentation, RVOS has been made remarkable progress where ReferFormer achieved the state-of-the-art performance. In this work, based on the strong baseline framework--ReferFormer, we propose several tricks to boost further, including cyclical learning rates, semi-supervised approach, and test-time augmentation inference. The improved ReferFormer ranks 2nd place on CVPR2022 Referring Youtube-VOS Challenge. | ['Hongbin Wang', 'Yuchen Hu', 'Fengliang Qi', 'Feng Zhang', 'Bo Yan', 'Zhuang Li', 'Leilei Cao'] | 2022-06-24 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.80957961e-01 1.30076140e-01 -5.23776174e-01 -4.32469666e-01
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8bab734b-f2fc-4da6-a517-ceab8dda0618 | brain-captioning-decoding-human-brain | 2305.11560 | null | https://arxiv.org/abs/2305.11560v1 | https://arxiv.org/pdf/2305.11560v1.pdf | Brain Captioning: Decoding human brain activity into images and text | Every day, the human brain processes an immense volume of visual information, relying on intricate neural mechanisms to perceive and interpret these stimuli. Recent breakthroughs in functional magnetic resonance imaging (fMRI) have enabled scientists to extract visual information from human brain activity patterns. In this study, we present an innovative method for decoding brain activity into meaningful images and captions, with a specific focus on brain captioning due to its enhanced flexibility as compared to brain decoding into images. Our approach takes advantage of cutting-edge image captioning models and incorporates a unique image reconstruction pipeline that utilizes latent diffusion models and depth estimation. We utilized the Natural Scenes Dataset, a comprehensive fMRI dataset from eight subjects who viewed images from the COCO dataset. We employed the Generative Image-to-text Transformer (GIT) as our backbone for captioning and propose a new image reconstruction pipeline based on latent diffusion models. The method involves training regularized linear regression models between brain activity and extracted features. Additionally, we incorporated depth maps from the ControlNet model to further guide the reconstruction process. We evaluate our methods using quantitative metrics for both generated captions and images. Our brain captioning approach outperforms existing methods, while our image reconstruction pipeline generates plausible images with improved spatial relationships. In conclusion, we demonstrate significant progress in brain decoding, showcasing the enormous potential of integrating vision and language to better understand human cognition. Our approach provides a flexible platform for future research, with potential applications in various fields, including neural art, style transfer, and portable devices. | ['Nicola Toschi', 'Rufin VanRullen', 'Tommaso Boccato', 'Furkan Ozcelik', 'Matteo Ferrante'] | 2023-05-19 | null | null | null | null | ['style-transfer', 'image-reconstruction', 'brain-decoding', 'brain-decoding'] | ['computer-vision', 'computer-vision', 'medical', 'miscellaneous'] | [ 6.15290999e-01 1.23098217e-01 8.39553624e-02 -5.44627488e-01
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a92146d3-5d75-4c36-bf0c-de580550f1fc | human-pose-forecasting-via-deep-markov-models | 1707.09240 | null | http://arxiv.org/abs/1707.09240v2 | http://arxiv.org/pdf/1707.09240v2.pdf | Human Pose Forecasting via Deep Markov Models | Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging due to the difficulty of estimating how long a
person continues an activity. To this end, our contributions are threefold: (i)
we propose a generative framework for poses using variational autoencoders
based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a
pose-based action classifier, which we argue better reflects the subjective
quality of pose forecasts than distance in coordinate space; (iii) last, for
evaluation of the new model, we introduce a 480,000-frame video dataset called
Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling
and disassembling furniture. We demonstrate promising results for our approach
on both Ikea FA and the existing NTU RGB+D dataset. | ['Tengda Han', 'Stephen Gould', 'Sam Toyer', 'Anoop Cherian'] | 2017-07-24 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-1.11971488e-02 1.22580856e-01 2.73244858e-01 -5.47793388e-01
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9dce6776-f91d-4d36-a861-13297452ff66 | im-sorry-dave-im-afraid-i-cant-do-that-deep-q | 1910.02078 | null | https://arxiv.org/abs/1910.02078v4 | https://arxiv.org/pdf/1910.02078v4.pdf | I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action | The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency controllers. For example, the range of motion and the angles of the motors of a robot can be limited to physical boundaries. Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes. In this paper, we propose a simple modification of a state-of-the-art deep RL algorithm (DQN), enabling learning from forbidden actions. To do so, the standard Q-learning update is enhanced with an extra safety loss inspired by structured classification. We empirically show that it reduces the number of hit constraints during the learning phase and accelerates convergence to near-optimal policies compared to using standard DQN. Experiments are done on a Visual Grid World Environment and Text-World domain. | ['Olivier Pietquin', 'Mathieu Seurin', 'Philippe Preux'] | 2019-10-04 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 1.14047162e-01 4.23039883e-01 -3.40248674e-01 -7.52610341e-02
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401324c3-acd3-4f6d-b0ba-7487281adbc8 | unveiling-class-labeling-structure-for | 2010.04873 | null | https://arxiv.org/abs/2010.04873v1 | https://arxiv.org/pdf/2010.04873v1.pdf | Unveiling Class-Labeling Structure for Universal Domain Adaptation | As a more practical setting for unsupervised domain adaptation, Universal Domain Adaptation (UDA) is recently introduced, where the target label set is unknown. One of the big challenges in UDA is how to determine the common label set shared by source and target domains, as there is simply no labeling available in the target domain. In this paper, we employ a probabilistic approach for locating the common label set, where each source class may come from the common label set with a probability. In particular, we propose a novel approach for evaluating the probability of each source class from the common label set, where this probability is computed by the prediction margin accumulated over the whole target domain. Then, we propose a simple universal adaptation network (S-UAN) by incorporating the probabilistic structure for the common label set. Finally, we analyse the generalization bound focusing on the common label set and explore the properties on the target risk for UDA. Extensive experiments indicate that S-UAN works well in different UDA settings and outperforms the state-of-the-art methods by large margins. | ['Haifeng Hu', 'Xiaofu Wu', 'Zhen Yang', 'Yueming Yin'] | 2020-10-10 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 1.90612912e-01 4.46468405e-02 -3.33330572e-01 -3.68587315e-01
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cd9d8105-bb55-4dd1-8173-e92a1ff7811c | piven-a-deep-neural-network-for-prediction | 2006.05139 | null | https://arxiv.org/abs/2006.05139v3 | https://arxiv.org/pdf/2006.05139v3.pdf | PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction | Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We present PIVEN, a deep neural network for producing both a PI and a value prediction. Our loss function expresses the value prediction as a function of the upper and lower bounds, thus ensuring that it falls within the interval without increasing model complexity. Moreover, our approach makes no assumptions regarding data distribution within the PI, making its value prediction more effective for various real-world problems. Experiments and ablation tests on known benchmarks show that our approach produces tighter uncertainty bounds than the current state-of-the-art approaches for producing PIs, while maintaining comparable performance to the state-of-the-art approach for value-prediction. Additionally, we go beyond previous work and include large image datasets in our evaluation, where PIVEN is combined with modern neural nets. | ['Lior Rokach', 'Gilad Katz', 'Eli Simhayev'] | 2020-06-09 | null | https://openreview.net/forum?id=qn_gk5j3PJ | https://openreview.net/pdf?id=qn_gk5j3PJ | null | ['value-prediction'] | ['computer-code'] | [ 1.23012647e-01 3.58245730e-01 -4.20954555e-01 -6.68498993e-01
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89957304-5839-4175-aa73-f999837e4bde | imbalanced-classification-via-a-tabular | 2204.08683 | null | https://arxiv.org/abs/2204.08683v1 | https://arxiv.org/pdf/2204.08683v1.pdf | Imbalanced Classification via a Tabular Translation GAN | When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class. We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. This translation mechanism encourages the synthesized samples to be close to the class boundary. Furthermore, we explore a selection criterion to retain the most useful of the synthesized samples. Experimental results using several downstream classifiers on a variety of tabular class-imbalanced datasets show that the proposed method improves average precision when compared to alternative re-weighting and oversampling techniques. | ['Amir Averbuch', 'Ofir Lindenbaum', 'Yoav Tulpan', 'Moshe Salhov', 'Jonathan Gradstein'] | 2022-04-19 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 4.57913429e-01 2.36617550e-01 -8.65867138e-01 -7.72909701e-01
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43619435-f180-4e8d-a17e-4da7696c1c5c | simara-a-database-for-key-value-information | 2304.13606 | null | https://arxiv.org/abs/2304.13606v1 | https://arxiv.org/pdf/2304.13606v1.pdf | SIMARA: a database for key-value information extraction from full pages | We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents. Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction. We propose a model based on the Transformer architecture trained for end-to-end information extraction and provide three sets for training, validation and testing, to ensure fair comparison with future works. The database is freely accessible at https://zenodo.org/record/7868059. | ['Christopher Kermorvant', 'Jean-François Moufflet', 'Mélodie Boillet', 'Solène Tarride'] | 2023-04-26 | null | null | null | null | ['handwriting-recognition', 'named-entity-recognition-ner', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-4.73401174e-02 -1.92617446e-01 -4.83188808e-01 -1.53096274e-01
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59e1a7be-4484-485c-ba92-6f180276b230 | speechglue-how-well-can-self-supervised | 2306.08374 | null | https://arxiv.org/abs/2306.08374v1 | https://arxiv.org/pdf/2306.08374v1.pdf | SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge? | Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition. More recently, speech SSL models have also been shown to be beneficial in advancing spoken language understanding tasks, implying that the SSL models have the potential to learn not only acoustic but also linguistic information. In this paper, we aim to clarify if speech SSL techniques can well capture linguistic knowledge. For this purpose, we introduce SpeechGLUE, a speech version of the General Language Understanding Evaluation (GLUE) benchmark. Since GLUE comprises a variety of natural language understanding tasks, SpeechGLUE can elucidate the degree of linguistic ability of speech SSL models. Experiments demonstrate that speech SSL models, although inferior to text-based SSL models, perform better than baselines, suggesting that they can acquire a certain amount of general linguistic knowledge from just unlabeled speech data. | ['Yukinori Honma', 'Marc Delcroix', 'Taichi Asami', 'Yusuke Ijima', 'Tomohiro Tanaka', 'Kohei Matsuura', 'Takafumi Moriya', 'Takanori Ashihara'] | 2023-06-14 | null | null | null | null | ['spoken-language-understanding', 'speaker-recognition', 'spoken-language-understanding'] | ['natural-language-processing', 'speech', 'speech'] | [ 2.61267781e-01 6.14682794e-01 -3.94659311e-01 -8.40338290e-01
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1330747d-2c2e-4d19-96d1-55c8d68e3e53 | structured-learning-for-temporal-relation | null | null | https://aclanthology.org/E17-1108 | https://aclanthology.org/E17-1108.pdf | Structured Learning for Temporal Relation Extraction from Clinical Records | We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task. | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.52675489e-02 9.07894447e-02 -1.02430904e+00 -6.21184468e-01
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026d5353-cb4e-4afc-87ff-92009936c718 | point-cloud-sampling-via-graph-balancing-and | 2103.06153 | null | https://arxiv.org/abs/2103.06153v1 | https://arxiv.org/pdf/2103.06153v1.pdf | Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment | 3D point cloud (PC) -- a collection of discrete geometric samples of a physical object's surface -- is typically large in size, which entails expensive subsequent operations like viewpoint image rendering and object recognition. Leveraging on recent advances in graph sampling, we propose a fast PC sub-sampling algorithm that reduces its size while preserving the overall object shape. Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-resolution PC, given 3D points chosen by a sampling matrix $\H$. We prove that minimizing a worst-case SR reconstruction error is equivalent to maximizing the smallest eigenvalue $\lambda_{\min}$ of a matrix $\H^{\top} \H + \mu \cL$, where $\cL$ is a symmetric, positive semi-definite matrix computed from the neighborhood graph connecting the 3D points. Instead, for fast computation we maximize a lower bound $\lambda^-_{\min}(\H^{\top} \H + \mu \cL)$ via selection of $\H$ in three steps. Interpreting $\cL$ as a generalized graph Laplacian matrix corresponding to an unbalanced signed graph $\cG$, we first approximate $\cG$ with a balanced graph $\cG_B$ with the corresponding generalized graph Laplacian matrix $\cL_B$. Second, leveraging on a recent theorem called Gershgorin disc perfect alignment (GDPA), we perform a similarity transform $\cL_p = \S \cL_B \S^{-1}$ so that Gershgorin disc left-ends of $\cL_p$ are all aligned at the same value $\lambda_{\min}(\cL_B)$. Finally, we perform PC sub-sampling on $\cG_B$ using a graph sampling algorithm to maximize $\lambda^-_{\min}(\H^{\top} \H + \mu \cL_p)$ in roughly linear time. Experimental results show that 3D points chosen by our algorithm outperformed competing schemes both numerically and visually in SR reconstruction quality. | ['Ivan Bajic', 'Gene Cheung', 'Chinthaka Dinesh'] | 2021-03-10 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.86067504e-01 5.15279830e-01 2.61855900e-01 1.52486712e-01
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92e990ef-1b1e-429a-8be2-82425943b619 | apollocar3d-a-large-3d-car-instance | 1811.12222 | null | http://arxiv.org/abs/1811.12222v2 | http://arxiv.org/pdf/1811.12222v2.pdf | ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving | Autonomous driving has attracted remarkable attention from both industry and
academia. An important task is to estimate 3D properties(e.g.translation,
rotation and shape) of a moving or parked vehicle on the road. This task, while
critical, is still under-researched in the computer vision community -
partially owing to the lack of large scale and fully-annotated 3D car database
suitable for autonomous driving research. In this paper, we contribute the
first large-scale database suitable for 3D car instance understanding -
ApolloCar3D. The dataset contains 5,277 driving images and over 60K car
instances, where each car is fitted with an industry-grade 3D CAD model with
absolute model size and semantically labelled keypoints. This dataset is above
20 times larger than PASCAL3D+ and KITTI, the current state-of-the-art. To
enable efficient labelling in 3D, we build a pipeline by considering 2D-3D
keypoint correspondences for a single instance and 3D relationship among
multiple instances. Equipped with such dataset, we build various baseline
algorithms with the state-of-the-art deep convolutional neural networks.
Specifically, we first segment each car with a pre-trained Mask R-CNN, and then
regress towards its 3D pose and shape based on a deformable 3D car model with
or without using semantic keypoints. We show that using keypoints significantly
improves fitting performance. Finally, we develop a new 3D metric jointly
considering 3D pose and 3D shape, allowing for comprehensive evaluation and
ablation study. By comparing with human performance we suggest several future
directions for further improvements. | ['Peng Wang', 'Rui Zhu', 'Ruigang Yang', 'Chenye Guan', 'Hao Su', 'Yuchao Dai', 'Xibin Song', 'Hongdong Li', 'Dingfu Zhou'] | 2018-11-29 | apollocar3d-a-large-3d-car-instance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Song_ApolloCar3D_A_Large_3D_Car_Instance_Understanding_Benchmark_for_Autonomous_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Song_ApolloCar3D_A_Large_3D_Car_Instance_Understanding_Benchmark_for_Autonomous_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-car-instance-understanding'] | ['computer-vision'] | [-2.36412048e-01 1.19825862e-01 -2.42871091e-01 -5.34137189e-01
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09df160a-d757-4489-9a9a-366a70ad2b81 | a-survey-on-conversational-search-and | 2211.15328 | null | https://arxiv.org/abs/2211.15328v1 | https://arxiv.org/pdf/2211.15328v1.pdf | A Survey on Conversational Search and Applications in Biomedicine | This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems. | ['Jiho Noh', 'Gowtham Reddy Gadireddy', 'Naga Sai Krishna Adatrao'] | 2022-11-28 | null | null | null | null | ['conversational-search', 'dialogue-management'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.11664140e-01 1.02533317e+00 -3.40878755e-01 -2.57533789e-01
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5873fb9f-098d-418d-bf97-51fa242b6651 | together-we-make-sense-learning-meta-sense | 2305.19092 | null | https://arxiv.org/abs/2305.19092v1 | https://arxiv.org/pdf/2305.19092v1.pdf | Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings | Sense embedding learning methods learn multiple vectors for a given ambiguous word, corresponding to its different word senses. For this purpose, different methods have been proposed in prior work on sense embedding learning that use different sense inventories, sense-tagged corpora and learning methods. However, not all existing sense embeddings cover all senses of ambiguous words equally well due to the discrepancies in their training resources. To address this problem, we propose the first-ever meta-sense embedding method -- Neighbour Preserving Meta-Sense Embeddings, which learns meta-sense embeddings by combining multiple independently trained source sense embeddings such that the sense neighbourhoods computed from the source embeddings are preserved in the meta-embedding space. Our proposed method can combine source sense embeddings that cover different sets of word senses. Experimental results on Word Sense Disambiguation (WSD) and Word-in-Context (WiC) tasks show that the proposed meta-sense embedding method consistently outperforms several competitive baselines. | ['Danushka Bollegala', 'Yi Zhou', 'Haochen Luo'] | 2023-05-30 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 3.12471241e-01 -1.21760763e-01 -4.59322244e-01 -2.69789308e-01
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2c65f50f-1e43-4128-82b5-f6e755c8ee57 | looking-and-listening-audio-guided-text | 2306.03482 | null | https://arxiv.org/abs/2306.03482v1 | https://arxiv.org/pdf/2306.03482v1.pdf | Looking and Listening: Audio Guided Text Recognition | Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In fact, the content of the text and its audio are naturally corresponding to each other, i.e., a single character error may result in a clear different pronunciation. In this paper, we propose the AudioOCR, a simple yet effective probabilistic audio decoder for mel spectrogram sequence prediction to guide the scene text recognition, which only participates in the training phase and brings no extra cost during the inference stage. The underlying principle of AudioOCR can be easily applied to the existing approaches. Experiments using 7 previous scene text recognition methods on 12 existing regular, irregular, and occluded benchmarks demonstrate our proposed method can bring consistent improvement. More importantly, through our experimentation, we show that AudioOCR possesses a generalizability that extends to more challenging scenarios, including recognizing non-English text, out-of-vocabulary words, and text with various accents. Code will be available at https://github.com/wenwenyu/AudioOCR. | ['Xiang Bai', 'Yuliang Liu', 'Xing Sun', 'Deqiang Jiang', 'Enming Zhang', 'Biao Yang', 'MingYu Liu', 'Wenwen Yu'] | 2023-06-06 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.70852673e-01 -5.91680527e-01 6.70886263e-02 -4.17834252e-01
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bc059a30-41d7-42af-b537-3421e878623e | tinydet-accurate-small-object-detection-in | 2304.03428 | null | https://arxiv.org/abs/2304.03428v1 | https://arxiv.org/pdf/2304.03428v1.pdf | TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors | Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors. To accurately detect small objects with limited computation, we propose a two-stage lightweight detection framework with extremely low computation complexity, termed as TinyDet. It enables high-resolution feature maps for dense anchoring to better cover small objects, proposes a sparsely-connected convolution for computation reduction, enhances the early stage features in the backbone, and addresses the feature misalignment problem for accurate small object detection. On the COCO benchmark, our TinyDet-M achieves 30.3 AP and 13.5 AP^s with only 991 MFLOPs, which is the first detector that has an AP over 30 with less than 1 GFLOPs; besides, TinyDet-S and TinyDet-L achieve promising performance under different computation limitation. | ['Xinggang Wang', 'Wenyu Liu', 'Yuan Li', 'Qian Zhang', 'Jiemin Fang', 'Tianheng Cheng', 'Shaoyu Chen'] | 2023-04-07 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.23536587e-01 -3.39920908e-01 -1.96578816e-01 -7.73383863e-03
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bae4da74-2fec-4687-81a5-3ff97d642153 | fine-grained-emotional-paraphrasing-along | 2212.03297 | null | https://arxiv.org/abs/2212.03297v1 | https://arxiv.org/pdf/2212.03297v1.pdf | Fine-Grained Emotional Paraphrasing along Emotion Gradients | Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, e.g., moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine grain following smooth variations in affective dimensions while preserving the meanings of the originals. We propose a framework for addressing this task by fine-tuning text-to-text Transformers through multi-task training. We enhance several widely used paraphrasing corpus by annotating the input and target texts with their fine-grained emotion labels. With these labels, fine-tuning text-to-text Transformers on these corpus entails multi-task training. Evaluations of the fine-tuned Transformers on separate test sets show that including fine-grained emotion labels in the paraphrase task significantly improve the chance of obtaining high-quality paraphrases of the desired emotions, i.e., more than doubling the number of exact matches of desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROGUE, and METEOR. | ['Justin Xie'] | 2022-10-30 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 6.79706559e-02 -1.39681876e-01 -2.19595045e-01 -6.65904462e-01
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546b73d4-6576-4d09-8ba9-0cca687d6a60 | progressive-contour-regression-for-arbitrary | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.pdf | Progressive Contour Regression for Arbitrary-Shape Scene Text Detection | State-of-the-art scene text detection methods usually model the text instance with local pixels or components from the bottom-up perspective and, therefore, are sensitive to noises and dependent on the complicated heuristic post-processing especially for arbitrary-shape texts. To relieve these two issues, instead, we propose to progressively evolve the initial text proposal to arbitrarily shaped text contours in a top-down manner. The initial horizontal text proposals are generated by estimating the center and size of texts. To reduce the range of regression, the first stage of the evolution predicts the corner points of oriented text proposals from the initial horizontal ones. In the second stage, the contours of the oriented text proposals are iteratively regressed to arbitrarily shaped ones. In the last iteration of this stage, we rescore the confidence of the final localized text by utilizing the cues from multiple contour points, rather than the single cue from the initial horizontal proposal center that may be out of arbitrary-shape text regions. Moreover, to facilitate the progressive contour evolution, we design a contour information aggregation mechanism to enrich the feature representation on text contours by considering both the circular topology and semantic context. Experiments conducted on CTW1500, Total-Text, ArT, and TD500 have demonstrated that the proposed method especially excels in line-level arbitrary-shape texts. Code is available at http://github.com/dpengwen/PCR. | ['Xiaochun Cao', 'Hua Zhang', 'Sanyi Zhang', 'Pengwen Dai'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['scene-text-detection'] | ['computer-vision'] | [ 1.42467454e-01 -1.43687531e-01 1.10444270e-01 -1.33350924e-01
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5bfa72ac-9bb4-49cc-b964-bd0cf20230f0 | unsupervised-deformable-medical-image | 2004.07624 | null | https://arxiv.org/abs/2004.07624v1 | https://arxiv.org/pdf/2004.07624v1.pdf | Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation | Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising approach for simplifying registration problems, and has been gradually applied to medical image registration. However, most existing deep learning registrations do not consider the problem that when the receptive field cannot cover the corresponding features in the moving image and the fixed image, it cannot output accurate displacement values. In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution. Multi-resolution and multi-convolution techniques can improve but fail to avoid this problem. In this study, we constructed pyramidal feature sets on moving and fixed images and used the warped moving and fixed features to estimate their "residual" deformation field at each scale, called the Pyramidal Residual Deformation Field Estimation module (PRDFE-Module). The "total" deformation field at each scale was computed by upsampling and weighted summing all the "residual" deformation fields at all its previous scales, which can effectively and accurately transfer the deformation fields from low resolution to high resolution and is used for warping the moving features at each scale. Simulation and real brain data results show that our method improves the accuracy of the registration and the rationality of the deformation field. | ['Yujia Zhou', 'Zhentai Lu', 'Yuhang Sun', 'Yi Wu', 'Wei Yang', 'Lei Zhao', 'Yaqin Liu', 'Shumao Pang', 'Qianjin Feng', 'Jun Cheng'] | 2020-04-16 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 5.90129010e-02 -4.57396954e-01 1.23909153e-01 -2.40387335e-01
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fdd26851-4566-45c1-b512-dc54ba0d7689 | softgroup-scalable-3d-instance-segmentation | 2209.08263 | null | https://arxiv.org/abs/2209.08263v2 | https://arxiv.org/pdf/2209.08263v2.pdf | Scalable SoftGroup for 3D Instance Segmentation on Point Clouds | This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping to obtain instance segmentation results. However, the errors stemming from hard decisions propagate into grouping that results in low overlaps of the predicted instances with the ground truth and substantial false positives. To address the aforementioned problems, SoftGroup allows each point to be associated with multiple classes to mitigate the problem stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Regarding scalability, the existing fast methods require computational time on the order of tens of seconds on large-scale scenes, which is unsatisfactory and far from applicable for real-time. Our finding is that the $k$-Nearest Neighbor ($k$-NN) module, which serves as the prerequisite of grouping, introduces computational bottleneck. SoftGroup is extended to resolve this computational bottleneck, which is referred to as SoftGroup++. The proposed SoftGroup++ reduces time complexity with octree $k$-NN and reduces search space with class-aware pyramid scaling and late devoxelization. Experimental results on various indoor and outdoor datasets demonstrate the efficacy and generality of the proposed SoftGroup and SoftGroup++. Their performances surpass the strongest baseline by a large margin (6\% $\sim$ 16\%) in terms of AP$_{50}$. On datasets with large-scale scenes, SoftGroup++ achieves 6$\times$ speed boost on average compared to SoftGroup. Furthermore, SoftGroup can be extended to perform object detection and panoptic segmentation with nontrivial improvements over existing methods. The source code and trained models are available at \url{https://github.com/thangvubk/SoftGroup}. | ['Chang D. Yoo', 'Junyeong Kim', 'Thanh Nguyen', 'Tung M. Luu', 'Kookhoi Kim', 'Thang Vu'] | 2022-09-17 | null | null | null | null | ['3d-instance-segmentation-1', 'panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.41105908e-01 2.59536922e-01 -1.82230741e-01 -4.81365830e-01
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39c7a6d6-740e-4a25-aae6-a0e50af919ac | patch-similarity-aware-data-free-quantization | 2203.02250 | null | https://arxiv.org/abs/2203.02250v3 | https://arxiv.org/pdf/2203.02250v3.pdf | Patch Similarity Aware Data-Free Quantization for Vision Transformers | Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constrained devices. Quantization is an effective approach to reduce model complexity, and data-free quantization, which can address data privacy and security concerns during model deployment, has received widespread interest. Unfortunately, all existing methods, such as BN regularization, were designed for convolutional neural networks and cannot be applied to vision transformers with significantly different model architectures. In this paper, we propose PSAQ-ViT, a Patch Similarity Aware data-free Quantization framework for Vision Transformers, to enable the generation of "realistic" samples based on the vision transformer's unique properties for calibrating the quantization parameters. Specifically, we analyze the self-attention module's properties and reveal a general difference (patch similarity) in its processing of Gaussian noise and real images. The above insights guide us to design a relative value metric to optimize the Gaussian noise to approximate the real images, which are then utilized to calibrate the quantization parameters. Extensive experiments and ablation studies are conducted on various benchmarks to validate the effectiveness of PSAQ-ViT, which can even outperform the real-data-driven methods. Code is available at: https://github.com/zkkli/PSAQ-ViT. | ['Qingyi Gu', 'Junrui Xiao', 'Mengjuan Chen', 'Liping Ma', 'Zhikai Li'] | 2022-03-04 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 9.60047469e-02 -1.47914767e-01 -1.40484750e-01 -4.39478785e-01
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03d3afca-5048-4dc2-80b7-5a25d91194f1 | collagan-collaborative-gan-for-missing-image | 1901.09764 | null | http://arxiv.org/abs/1901.09764v3 | http://arxiv.org/pdf/1901.09764v3.pdf | CollaGAN : Collaborative GAN for Missing Image Data Imputation | In many applications requiring multiple inputs to obtain a desired output, if
any of the input data is missing, it often introduces large amounts of bias.
Although many techniques have been developed for imputing missing data, the
image imputation is still difficult due to complicated nature of natural
images. To address this problem, here we proposed a novel framework for missing
image data imputation, called Collaborative Generative Adversarial Network
(CollaGAN). CollaGAN converts an image imputation problem to a multi-domain
images-to-image translation task so that a single generator and discriminator
network can successfully estimate the missing data using the remaining clean
data set. We demonstrate that CollaGAN produces the images with a higher visual
quality compared to the existing competing approaches in various image
imputation tasks. | ['Jong Chul Ye', 'Won-Jin Moon', 'Dongwook Lee', 'Junyoung Kim'] | 2019-01-28 | null | null | null | null | ['image-imputation'] | ['computer-vision'] | [ 7.75983870e-01 2.63230745e-02 7.99575150e-02 -4.94573891e-01
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a5aa716d-678c-4ce0-a5ea-b06d1ab9e4df | revisiting-weakly-supervised-pre-training-of | 2201.08371 | null | https://arxiv.org/abs/2201.08371v2 | https://arxiv.org/pdf/2201.08371v2.pdf | Revisiting Weakly Supervised Pre-Training of Visual Perception Models | Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent studies suggest that large-scale weakly supervised pre-training can outperform fully supervised approaches. This paper revisits weakly-supervised pre-training of models using hashtag supervision with modern versions of residual networks and the largest-ever dataset of images and corresponding hashtags. We study the performance of the resulting models in various transfer-learning settings including zero-shot transfer. We also compare our models with those obtained via large-scale self-supervised learning. We find our weakly-supervised models to be very competitive across all settings, and find they substantially outperform their self-supervised counterparts. We also include an investigation into whether our models learned potentially troubling associations or stereotypes. Overall, our results provide a compelling argument for the use of weakly supervised learning in the development of visual recognition systems. Our models, Supervised Weakly through hashtAGs (SWAG), are available publicly. | ['Laurens van der Maaten', 'Piotr Dollár', 'Ross Girshick', 'Dhruv Mahajan', 'Raj Prateek Kosaraju', 'Bugra Gedik', 'Vinicius de Freitas Reis', 'Aaron Adcock', 'Laura Gustafson', 'Mannat Singh'] | 2022-01-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.21983874e-01 5.31970203e-01 -5.50910890e-01 -8.59734952e-01
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f703187d-4143-44c2-aa22-166aed7b6cde | qald-9-plus-a-multilingual-dataset-for | 2202.00120 | null | https://arxiv.org/abs/2202.00120v2 | https://arxiv.org/pdf/2202.00120v2.pdf | QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers | The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual aspect of accessibility of KGQA systems, we identified several ongoing challenges. One of them is the lack of multilingual KGQA benchmarks. In this work, we extend one of the most popular KGQA benchmarks - QALD-9 by introducing high-quality questions' translations to 8 languages provided by native speakers, and transferring the SPARQL queries of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased. Five of the languages - Armenian, Ukrainian, Lithuanian, Bashkir and Belarusian - to our best knowledge were never considered in KGQA research community before. The latter two of the languages are considered as "endangered" by UNESCO. We call the extended dataset QALD-9-plus and made it available online https://github.com/Perevalov/qald_9_plus. | ['Andreas Both', 'Ricardo Usbeck', 'Dennis Diefenbach', 'Aleksandr Perevalov'] | 2022-01-31 | qald-9-plus-a-multilingual-dataset-for-1 | https://arxiv.org/abs/2202.00120 | https://arxiv.org/pdf/2202.00120.pdf | null | ['graph-question-answering'] | ['graphs'] | [-8.42581868e-01 5.66067278e-01 8.09452906e-02 -9.52104479e-02
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f72e7fc9-1a64-4878-bec4-bd058ec1046f | information-theoretic-hashing-for-zero-shot | 2209.12491 | null | https://arxiv.org/abs/2209.12491v1 | https://arxiv.org/pdf/2209.12491v1.pdf | Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval | Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval problem among heterogenous data from unseen classes. Typically, to guarantee generalization, the pre-defined class embeddings from natural language processing (NLP) models are used to build a common space. In this paper, instead of using an extra NLP model to define a common space beforehand, we consider a totally different way to construct (or learn) a common hamming space from an information-theoretic perspective. We term our model the Information-Theoretic Hashing (ITH), which is composed of two cascading modules: an Adaptive Information Aggregation (AIA) module; and a Semantic Preserving Encoding (SPE) module. Specifically, our AIA module takes the inspiration from the Principle of Relevant Information (PRI) to construct a common space that adaptively aggregates the intrinsic semantics of different modalities of data and filters out redundant or irrelevant information. On the other hand, our SPE module further generates the hashing codes of different modalities by preserving the similarity of intrinsic semantics with the element-wise Kullback-Leibler (KL) divergence. A total correlation regularization term is also imposed to reduce the redundancy amongst different dimensions of hash codes. Sufficient experiments on three benchmark datasets demonstrate the superiority of the proposed ITH in ZS-CMR. Source code is available in the supplementary material. | ['Xinge You', 'Duanquan Xu', 'Shujian Yu', 'Yufeng Shi'] | 2022-09-26 | null | null | null | null | ['zero-shot-cross-modal-retrieval'] | ['miscellaneous'] | [ 2.07916826e-01 -1.27056018e-01 -8.55012983e-03 -3.34206551e-01
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65275d00-f534-43b5-9f56-bf3679039372 | bottom-up-broadcast-neural-network-for-music | 1901.08928 | null | http://arxiv.org/abs/1901.08928v1 | http://arxiv.org/pdf/1901.08928v1.pdf | Bottom-up Broadcast Neural Network For Music Genre Classification | Music genre recognition based on visual representation has been successfully
explored over the last years. Recently, there has been increasing interest in
attempting convolutional neural networks (CNNs) to achieve the task. However,
most of existing methods employ the mature CNN structures proposed in image
recognition without any modification, which results in the learning features
that are not adequate for music genre classification. Faced with the challenge
of this issue, we fully exploit the low-level information from spectrograms of
audios and develop a novel CNN architecture in this paper. The proposed CNN
architecture takes the long contextual information into considerations, which
transfers more suitable information for the decision-making layer. Various
experiments on several benchmark datasets, including GTZAN, Ballroom, and
Extended Ballroom, have verified the excellent performances of the proposed
neural network. Codes and model will be available at
"ttps://github.com/CaifengLiu/music-genre-classification". | ['Shenglan Liu', 'Huibing Wang', 'Guochao Liu', 'Caifeng Liu', 'Lin Feng'] | 2019-01-24 | null | null | null | null | ['genre-classification', 'music-genre-recognition'] | ['computer-vision', 'music'] | [ 9.01224390e-02 -5.53963602e-01 -3.43898758e-02 -1.50334090e-01
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60549a6f-03a0-43ba-ba87-1f541a09f4d7 | contrast-limited-adaptive-histogram | 2109.00886 | null | https://arxiv.org/abs/2109.00886v1 | https://arxiv.org/pdf/2109.00886v1.pdf | Contrast Limited Adaptive Histogram Equalization (CLAHE) Approach for Enhancement of the Microstructures of Friction Stir Welded Joints | Image processing algorithms are finding various applications in manufacturing and materials industries such as identification of cracks in the fabricated samples, calculating the geometrical properties of the given microstructure, presence of surface defects, etc. The present work deals with the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm for improving the quality of the microstructure images of the Friction Stir Welded joints. The obtained results showed that the obtained value of quantitative metric features such as Entropy value and RMS Contrast value were high which resulted in enhanced microstructure images. | ['Akshansh Mishra'] | 2021-08-15 | null | null | null | null | ['local-color-enhancement'] | ['computer-vision'] | [ 1.78628623e-01 -3.71260792e-01 5.99328995e-01 -2.28669420e-01
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f5f33c18-38bc-4fd2-8e0c-f1d16185e619 | improving-document-clustering-by-removing | null | null | https://aclanthology.org/W17-4416 | https://aclanthology.org/W17-4416.pdf | Improving Document Clustering by Removing Unnatural Language | Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can bean important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of un-natural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various for-mats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that re-moving unnatural language components gives an absolute improvement in document cluster-ing by up to 15{\%}. Our corpus and tool are publicly available | ['Jinho D. Choi', 'James Allan', 'Myungha Jang'] | 2017-09-01 | null | null | null | ws-2017-9 | ['document-layout-analysis'] | ['computer-vision'] | [ 1.45365368e-03 1.50749967e-01 -2.83851862e-01 -2.96455592e-01
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0adde4f2-ba8b-4b57-8445-550d20e3fe69 | mt-clinical-bert-scaling-clinical-information | 2004.10220 | null | https://arxiv.org/abs/2004.10220v1 | https://arxiv.org/pdf/2004.10220v1.pdf | MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning | Clinical notes contain an abundance of important but not-readily accessible information about patients. Systems to automatically extract this information rely on large amounts of training data for which their exists limited resources to create. Furthermore, they are developed dis-jointly; meaning that no information can be shared amongst task-specific systems. This bottle-neck unnecessarily complicates practical application, reduces the performance capabilities of each individual solution and associates the engineering debt of managing multiple information extraction systems. We address these challenges by developing Multitask-Clinical BERT: a single deep learning model that simultaneously performs eight clinical tasks spanning entity extraction, PHI identification, language entailment and similarity by sharing representations amongst tasks. We find our single system performs competitively with all state-the-art task-specific systems while also benefiting from massive computational benefits at inference. | ['Bridget T. McInnes', 'Andriy Mulyar'] | 2020-04-21 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 1.32818192e-01 2.45242998e-01 -1.09345175e-01 -2.33401254e-01
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00d0c4fe-5c15-4b06-8559-707b8ffd60b3 | towards-robust-visual-information-extraction | 2102.06732 | null | https://arxiv.org/abs/2102.06732v1 | https://arxiv.org/pdf/2102.06732v1.pdf | Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution | Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust visual information extraction system (VIES) towards real-world scenarios, which is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario. | ['Mingxiang Cai', 'Yaqiang Wu', 'Qianying Wang', 'Shuaitao Zhang', 'Jiaxin Zhang', 'Guozhi Tang', 'Lianwen Jin', 'Chongyu Liu', 'Jiapeng Wang'] | 2021-01-24 | null | null | null | null | ['3d-feature-matching', 'text-spotting'] | ['computer-vision', 'computer-vision'] | [ 4.99329537e-01 -4.28762287e-01 -6.59731105e-02 -1.09599404e-01
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0f4b6635-6162-4c58-9f66-f1f243559d91 | automatic-calibration-and-error-correction | 2306.16564 | null | https://arxiv.org/abs/2306.16564v2 | https://arxiv.org/pdf/2306.16564v2.pdf | LLM Calibration and Automatic Hallucination Detection via Pareto Optimal Self-supervision | Large language models (LLMs) have demonstrated remarkable capabilities out of box for a wide range of applications, yet accuracy still remains a major growth area, especially in mission-critical domains such as biomedicine. An effective method to calibrate the confidence level on LLM responses is essential to automatically detect errors and facilitate human-in-the-loop verification. An important source of calibration signals stems from expert-stipulated programmatic supervision, which is often available at low cost but has its own limitations such as noise and coverage. In this paper, we introduce a Pareto optimal self-supervision framework that can leverage available programmatic supervision to systematically calibrate LLM responses by producing a risk score for every response, without any additional manual efforts. This is accomplished by learning a harmonizer model to align LLM output with other available supervision sources, which would assign higher risk scores to more uncertain LLM responses and facilitate error correction. Experiments on standard relation extraction tasks in biomedical and general domains demonstrate the promise of this approach, with our proposed risk scores highly correlated with the real error rate of LLMs. For the most uncertain test instances, dynamic prompting based on our proposed risk scores results in significant accuracy improvement for off-the-shelf LLMs, boosting GPT-3 results past state-of-the-art (SOTA) weak supervision and GPT-4 results past SOTA supervised results on challenging evaluation datasets. | ['Hoifung Poon', 'J. Samuel Preston', 'Mu Wei', 'Theodore Zhao'] | 2023-06-28 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 5.66897810e-01 5.10772288e-01 -4.93438244e-01 -8.34649146e-01
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dc157ef6-6bcd-4973-907a-34c23518005b | sequential-matching-network-a-new | 1612.01627 | null | http://arxiv.org/abs/1612.01627v2 | http://arxiv.org/pdf/1612.01627v2.pdf | Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots | We study response selection for multi-turn conversation in retrieval-based
chatbots. Existing work either concatenates utterances in context or matches a
response with a highly abstract context vector finally, which may lose
relationships among utterances or important contextual information. We propose
a sequential matching network (SMN) to address both problems. SMN first matches
a response with each utterance in the context on multiple levels of
granularity, and distills important matching information from each pair as a
vector with convolution and pooling operations. The vectors are then
accumulated in a chronological order through a recurrent neural network (RNN)
which models relationships among utterances. The final matching score is
calculated with the hidden states of the RNN. An empirical study on two public
data sets shows that SMN can significantly outperform state-of-the-art methods
for response selection in multi-turn conversation. | ['Zhoujun Li', 'Yu Wu', 'Ming Zhou', 'Wei Wu', 'Chen Xing'] | 2016-12-06 | sequential-matching-network-a-new-1 | https://aclanthology.org/P17-1046 | https://aclanthology.org/P17-1046.pdf | acl-2017-7 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 4.22777206e-01 -1.43809021e-01 -1.51081949e-01 -8.13826919e-01
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2fe358fb-ea5e-45ff-bf22-06085eca4812 | torchosr-a-pytorch-extension-package-for-open | 2305.09646 | null | https://arxiv.org/abs/2305.09646v1 | https://arxiv.org/pdf/2305.09646v1.pdf | torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python | The article presents the torchosr package - a Python package compatible with PyTorch library - offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for the Open Set Recognition task (where some classes are considered unknown and used only in the testing process) and additional tools to handle datasets and methods. The main goal of the package proposal is to simplify and promote the correct experimental evaluation, where experiments are carried out on a large number of derivative sets with various Openness and class-to-category assignments. The authors hope that state-of-the-art methods available in the package will become a source of a correct and open-source implementation of the relevant solutions in the domain. | ['Pawel Ksieniewicz', 'Joanna Komorniczak'] | 2023-05-16 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-7.37421960e-02 9.01238397e-02 3.35950851e-01 -7.44382501e-01
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e7867e70-12ee-4648-a00a-38a1412be910 | unsupervised-domain-adaptation-for-training | 2303.12424 | null | https://arxiv.org/abs/2303.12424v1 | https://arxiv.org/pdf/2303.12424v1.pdf | Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning | Event-based cameras offer reliable measurements for preforming computer vision tasks in high-dynamic range environments and during fast motion maneuvers. However, adopting deep learning in event-based vision faces the challenge of annotated data scarcity due to recency of event cameras. Transferring the knowledge that can be obtained from conventional camera annotated data offers a practical solution to this challenge. We develop an unsupervised domain adaptation algorithm for training a deep network for event-based data image classification using contrastive learning and uncorrelated conditioning of data. Our solution outperforms the existing algorithms for this purpose. | ['Mohammad Rostami', 'Dayuan Jian'] | 2023-03-22 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.36528450e-01 -4.33757901e-01 -1.98386475e-01 -4.45411026e-01
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21f036a8-127e-4e4e-aa22-b0b747bd1141 | unsupervised-learning-of-visual-features-by | 2006.09882 | null | https://arxiv.org/abs/2006.09882v5 | https://arxiv.org/pdf/2006.09882v5.pdf | Unsupervised Learning of Visual Features by Contrasting Cluster Assignments | Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks. | ['Priya Goyal', 'Julien Mairal', 'Armand Joulin', 'Piotr Bojanowski', 'Mathilde Caron', 'Ishan Misra'] | 2020-06-17 | null | http://proceedings.neurips.cc/paper/2020/hash/70feb62b69f16e0238f741fab228fec2-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/70feb62b69f16e0238f741fab228fec2-Paper.pdf | neurips-2020-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.48020476e-01 -3.84894982e-02 1.32470969e-02 -3.06272835e-01
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cabe27e8-743e-4de3-b806-20b4353293d5 | exposing-cross-lingual-lexical-knowledge-from | 2205.00267 | null | https://arxiv.org/abs/2205.00267v2 | https://arxiv.org/pdf/2205.00267v2.pdf | Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders | Pretrained multilingual language models (LMs) can be successfully transformed into multilingual sentence encoders (SEs; e.g., LaBSE, xMPNet) via additional fine-tuning or model distillation with parallel data. However, it remains unclear how to best leverage them to represent sub-sentence lexical items (i.e., words and phrases) in cross-lingual lexical tasks. In this work, we probe SEs for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs. We also devise a simple yet efficient method for exposing the cross-lingual lexical knowledge by means of additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. Using bilingual lexical induction (BLI), cross-lingual lexical semantic similarity, and cross-lingual entity linking as lexical probing tasks, we report substantial gains on standard benchmarks (e.g., +10 Precision@1 points in BLI). The results indicate that the SEs such as LaBSE can be 'rewired' into effective cross-lingual lexical encoders via the contrastive learning procedure, and that they contain more cross-lingual lexical knowledge than what 'meets the eye' when they are used as off-the-shelf SEs. This way, we also provide an effective tool for harnessing 'covert' multilingual lexical knowledge hidden in multilingual sentence encoders. | ['Anna Korhonen', 'Edoardo Maria Ponti', 'Nigel Collier', 'Fangyu Liu', 'Goran Glavaš', 'Ivan Vulić'] | 2022-04-30 | null | null | null | null | ['cross-lingual-entity-linking', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-1.20839290e-01 1.25913024e-01 -7.43156493e-01 -3.11979473e-01
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8a2661d6-db16-4b27-b738-8094ed106398 | topological-biomarkers-for-real-time | 2211.02523 | null | https://arxiv.org/abs/2211.02523v1 | https://arxiv.org/pdf/2211.02523v1.pdf | Topological biomarkers for real-time detection of epileptic seizures | Automated seizure detection is a fundamental problem in computational neuroscience towards diagnosis and treatment's improvement of epileptic disease. We propose a real-time computational method for automated tracking and detection of epileptic seizures from raw neurophysiological recordings. Our mechanism is based on the topological analysis of the sliding-window embedding of the time series derived from simultaneously recorded channels. We extract topological biomarkers from the signals via the computation of the persistent homology of time-evolving topological spaces. Remarkably, the proposed biomarkers robustly captures the change in the brain dynamics during the ictal state. We apply our methods in different types of signals including scalp and intracranial EEG and MEG, in patients during interictal and ictal states, showing high accuracy in a range of clinical situations. | ['Diego Mateos', 'Ximena Fernandez'] | 2022-11-04 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 9.37476978e-02 -3.49481136e-01 5.48684657e-01 -1.02707386e-01
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71145bfd-121b-4aea-a2a7-52c8494ec7b5 | a-joint-model-and-data-driven-method-for | 2303.17241 | null | https://arxiv.org/abs/2303.17241v1 | https://arxiv.org/pdf/2303.17241v1.pdf | A Joint Model and Data Driven Method for Distributed Estimation | This paper considers the problem of distributed estimation in wireless sensor networks (WSN), which is anticipated to support a wide range of applications such as the environmental monitoring, weather forecasting, and location estimation. To this end, we propose a joint model and data driven distributed estimation method by designing the optimal quantizers and fusion center (FC) based on the Bayesian and minimum mean square error (MMSE) criterions. First, universal mean square error (MSE) lower bound for the quantization-based distributed estimation is derived and adopted as the design metric for the quantizers. Then, the optimality of the mean-fusion operation for the FC with MMSE criterion is proved. Next, by exploiting different levels of the statistic information of the desired parameter and observation noise, a joint model and data driven method is proposed to train parts of the quantizer and FC modules as deep neural networks (DNNs), and two loss functions derived from the MMSE criterion are adopted for the sequential training scheme. Furthermore, we extend the above results to the case with multi-bit quantizers, considering both the parallel and one-hot quantization schemes. Finally, simulation results reveal that the proposed method outperforms the state-of-the-art schemes in typical scenarios. | ['Chuan Huang', 'Ran Li', 'Meng He'] | 2023-03-30 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 1.79250345e-01 4.18086946e-02 -1.73730835e-01 -4.48077202e-01
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35dee570-9011-4382-9cfc-006c277952bb | explainable-representation-learning-of-small | 2306.05694 | null | https://arxiv.org/abs/2306.05694v1 | https://arxiv.org/pdf/2306.05694v1.pdf | Explainable Representation Learning of Small Quantum States | Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned representation provides insights into which features of the data are relevant for the task at hand. In the context of quantum physics, training models to describe quantum states without human intervention offers a promising approach to gaining insight into how machines represent complex quantum states. The ability to interpret the learned representation may offer a new perspective on non-trivial features of quantum systems and their efficient representation. We train a generative model on two-qubit density matrices generated by a parameterized quantum circuit. In a series of computational experiments, we investigate the learned representation of the model and its internal understanding of the data. We observe that the model learns an interpretable representation which relates the quantum states to their underlying entanglement characteristics. In particular, our results demonstrate that the latent representation of the model is directly correlated with the entanglement measure concurrence. The insights from this study represent proof of concept towards interpretable machine learning of quantum states. Our approach offers insight into how machines learn to represent small-scale quantum systems autonomously. | ['Evert van Nieuwenburg', 'Felix Frohnert'] | 2023-06-09 | null | null | null | null | ['interpretable-machine-learning', 'feature-engineering'] | ['methodology', 'methodology'] | [ 3.77676427e-01 4.31157947e-01 -1.87152550e-01 -4.66873348e-01
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2b85e9ff-3780-4af2-8461-09b079dcd2ab | learning-comment-generation-by-leveraging | 1810.12264 | null | http://arxiv.org/abs/1810.12264v2 | http://arxiv.org/pdf/1810.12264v2.pdf | Learning Comment Generation by Leveraging User-Generated Data | Existing models on open-domain comment generation are difficult to train, and
they produce repetitive and uninteresting responses. The problem is due to
multiple and contradictory responses from a single article, and by the rigidity
of retrieval methods. To solve this problem, we propose a combined approach to
retrieval and generation methods. We propose an attentive scorer to retrieve
informative and relevant comments by leveraging user-generated data. Then, we
use such comments, together with the article, as input for a
sequence-to-sequence model with copy mechanism. We show the robustness of our
model and how it can alleviate the aforementioned issue by using a large scale
comment generation dataset. The result shows that the proposed generative model
significantly outperforms strong baseline such as Seq2Seq with attention and
Information Retrieval models by around 27 and 30 BLEU-1 points respectively. | ['Genta Indra Winata', 'Zhaojiang Lin', 'Pascale Fung'] | 2018-10-29 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 3.43958944e-01 2.57298023e-01 1.25995185e-02 -1.85641989e-01
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48af821e-4ffb-421f-95ba-4b67aba68bb5 | mycorrhiza-genotype-assignment | 2010.09483 | null | https://arxiv.org/abs/2010.09483v1 | https://arxiv.org/pdf/2010.09483v1.pdf | Mycorrhiza: Genotype Assignment usingPhylogenetic Networks | Motivation The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection and biodiversity monitoring. Existing approaches perform well under ideal conditions but are sensitive to a variety of common violations of the assumptions they rely on. Results In this article, we introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Those features are then used as input to a Random Forests classifier. The classification accuracy was assessed on multiple published empirical SNP, microsatellite or consensus sequence datasets with wide ranges of size, geographical distribution and population structure and on simulated datasets. It compared favorably against widely used assessment tests or mixture analysis methods such as STRUCTURE and Admixture, and against another machine-learning based approach using principal component analysis for dimensionality reduction. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium. Moreover, the phylogenetic network approach estimates mixture proportions with good accuracy. | ['Mathieu Blanchette', 'Richard C. Hamelin', 'Jeremy Georges-Filteau'] | 2020-10-14 | null | null | null | null | ['population-assignment'] | ['medical'] | [ 6.05316877e-01 -5.12452304e-01 -3.04287434e-01 -3.57184917e-01
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07240c9e-8292-4427-b610-83c74b9da834 | hard-attention-for-scalable-image | 2102.10212 | null | https://arxiv.org/abs/2102.10212v2 | https://arxiv.org/pdf/2102.10212v2.pdf | Hard-Attention for Scalable Image Classification | Can we leverage high-resolution information without the unsustainable quadratic complexity to input scale? We propose Traversal Network (TNet), a novel multi-scale hard-attention architecture, which traverses image scale-space in a top-down fashion, visiting only the most informative image regions along the way. TNet offers an adjustable trade-off between accuracy and complexity, by changing the number of attended image locations. We compare our model against hard-attention baselines on ImageNet, achieving higher accuracy with less resources (FLOPs, processing time and memory). We further test our model on fMoW dataset, where we process satellite images of size up to $896 \times 896$ px, getting up to $2.5$x faster processing compared to baselines operating on the same resolution, while achieving higher accuracy as well. TNet is modular, meaning that most classification models could be adopted as its backbone for feature extraction, making the reported performance gains orthogonal to benefits offered by existing optimized deep models. Finally, hard-attention guarantees a degree of interpretability to our model's predictions, without any extra cost beyond inference. Code is available at $\href{https://github.com/Tpap/TNet}{github.com/Tpap/TNet}$. | ['Nasir Memon', 'Paweł Korus', 'Athanasios Papadopoulos'] | 2021-02-20 | null | http://proceedings.neurips.cc/paper/2021/hash/7b7916dd2de56297aa29cccb2bbf48d4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/7b7916dd2de56297aa29cccb2bbf48d4-Paper.pdf | neurips-2021-12 | ['deep-attention', 'hard-attention', 'deep-attention'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 1.15900889e-01 3.32541764e-01 -3.96838002e-02 -4.94455099e-01
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5ab212fd-15e7-4a7b-9677-0e1f77c05083 | maskvit-masked-visual-pre-training-for-video | 2206.11894 | null | https://arxiv.org/abs/2206.11894v2 | https://arxiv.org/pdf/2206.11894v2.pdf | MaskViT: Masked Visual Pre-Training for Video Prediction | The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our approach, named MaskViT, is based on two simple design decisions. First, for memory and training efficiency, we use two types of window attention: spatial and spatiotemporal. Second, during training, we mask a variable percentage of tokens instead of a fixed mask ratio. For inference, MaskViT generates all tokens via iterative refinement where we incrementally decrease the masking ratio following a mask scheduling function. On several datasets we demonstrate that MaskViT outperforms prior works in video prediction, is parameter efficient, and can generate high-resolution videos (256x256). Further, we demonstrate the benefits of inference speedup (up to 512x) due to iterative decoding by using MaskViT for planning on a real robot. Our work suggests that we can endow embodied agents with powerful predictive models by leveraging the general framework of masked visual modeling with minimal domain knowledge. | ['Li Fei-Fei', 'Roberto Martín-Martín', 'Jiajun Wu', 'Yunzhi Zhang', 'Stephen Tian', 'Agrim Gupta'] | 2022-06-23 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.41300815e-01 5.57910621e-01 -1.83338657e-01 -2.36344308e-01
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97f458a4-885d-4a36-9b1a-c8723dc5a6e9 | multimodal-deep-learning | 2301.04856 | null | https://arxiv.org/abs/2301.04856v1 | https://arxiv.org/pdf/2301.04856v1.pdf | Multimodal Deep Learning | This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet. | ['Matthias Aßenmacher', 'Daniel Schalk', 'Rasmus Hvingelby', 'Christian Heumann', 'Jann Goschenhofer', 'Karol Urbanczyk', 'Rickmer Schulte', 'Maximilian Schneider', 'Nadja Sauter', 'Marco Moldovan', 'Christopher Marquardt', 'Giacomo Loss', 'Philipp Koch', 'Steffen Jauch-Walser', 'Vladana Djakovic', 'Luyang Chu', 'Cem Akkus'] | 2023-01-12 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 3.25547695e-01 3.07008445e-01 -3.87703270e-01 -4.75143015e-01
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9f3ecfc2-7449-4436-8c1a-70af61fbc0b3 | neural-poisson-indicator-functions-for-neural | 2211.14249 | null | https://arxiv.org/abs/2211.14249v1 | https://arxiv.org/pdf/2211.14249v1.pdf | Neural Poisson: Indicator Functions for Neural Fields | Implicit neural field generating signed distance field representations (SDFs) of 3D shapes have shown remarkable progress in 3D shape reconstruction and generation. We introduce a new paradigm for neural field representations of 3D scenes; rather than characterizing surfaces as SDFs, we propose a Poisson-inspired characterization for surfaces as indicator functions optimized by neural fields. Crucially, for reconstruction of real scan data, the indicator function representation enables simple and effective constraints based on common range sensing inputs, which indicate empty space based on line of sight. Such empty space information is intrinsic to the scanning process, and incorporating this knowledge enables more accurate surface reconstruction. We show that our approach demonstrates state-of-the-art reconstruction performance on both synthetic and real scanned 3D scene data, with 9.5% improvement in Chamfer distance over state of the art. | ['Matthias Nießner', 'Angela Dai'] | 2022-11-25 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 7.81611264e-01 4.39057946e-01 2.52042234e-01 -4.91465718e-01
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746c02fb-d48a-4be6-bc8a-ba6211b8256a | crash-raw-audio-score-based-generative | 2106.07431 | null | https://arxiv.org/abs/2106.07431v1 | https://arxiv.org/pdf/2106.07431v1.pdf | CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis | In this paper, we propose a novel score-base generative model for unconditional raw audio synthesis. Our proposal builds upon the latest developments on diffusion process modeling with stochastic differential equations, which already demonstrated promising results on image generation. We motivate novel heuristics for the choice of the diffusion processes better suited for audio generation, and consider the use of a conditional U-Net to approximate the score function. While previous approaches on diffusion models on audio were mainly designed as speech vocoders in medium resolution, our method termed CRASH (Controllable Raw Audio Synthesis with High-resolution) allows us to generate short percussive sounds in 44.1kHz in a controllable way. Through extensive experiments, we showcase on a drum sound generation task the numerous sampling schemes offered by our method (unconditional generation, deterministic generation, inpainting, interpolation, variations, class-conditional sampling) and propose the class-mixing sampling, a novel way to generate "hybrid" sounds. Our proposed method closes the gap with GAN-based methods on raw audio, while offering more flexible generation capabilities with lighter and easier-to-train models. | ['Gaëtan Hadjeres', 'Simon Rouard'] | 2021-06-14 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.70858496e-01 3.19256574e-01 4.49903458e-01 1.53377056e-01
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704e598b-2d70-4115-afdb-3bf5fa97df54 | sparse-bayesian-state-space-and-time-varying | 2207.12147 | null | https://arxiv.org/abs/2207.12147v1 | https://arxiv.org/pdf/2207.12147v1.pdf | Sparse Bayesian State-Space and Time-Varying Parameter Models | In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance selection for TVP models by using a non-centered parametrization. We discuss efficient MCMC estimation and provide an application to US inflation modeling. | ['Peter Knaus', 'Sylvia Frühwirth-Schnatter'] | 2022-07-25 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.64514178e-01 -4.58905190e-01 4.09683958e-03 -6.49395049e-01
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b62fd6a8-5630-42c5-a20f-871b1f589c84 | slca-slow-learner-with-classifier-alignment | 2303.05118 | null | https://arxiv.org/abs/2303.05118v1 | https://arxiv.org/pdf/2303.05118v1.pdf | SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model | The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data. Although most existing works are established on the premise of learning from scratch, growing efforts have been devoted to incorporating the benefits of pre-training. However, how to adaptively exploit the pre-trained knowledge for each incremental task while maintaining its generalizability remains an open question. In this work, we present an extensive analysis for continual learning on a pre-trained model (CLPM), and attribute the key challenge to a progressive overfitting problem. Observing that selectively reducing the learning rate can almost resolve this issue in the representation layer, we propose a simple but extremely effective approach named Slow Learner with Classifier Alignment (SLCA), which further improves the classification layer by modeling the class-wise distributions and aligning the classification layers in a post-hoc fashion. Across a variety of scenarios, our proposal provides substantial improvements for CLPM (e.g., up to 49.76%, 50.05%, 44.69% and 40.16% on Split CIFAR-100, Split ImageNet-R, Split CUB-200 and Split Cars-196, respectively), and thus outperforms state-of-the-art approaches by a large margin. Based on such a strong baseline, critical factors and promising directions are analyzed in-depth to facilitate subsequent research. | ['Yunchao Wei', 'Ling Chen', 'Guoliang Kang', 'Liyuan Wang', 'Gengwei Zhang'] | 2023-03-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.84508437e-01 -2.31052428e-01 -2.72284597e-01 -5.55828273e-01
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991832f3-1a26-4df7-a6ea-e0a11665ab56 | neural-network-for-determining-an-asteroid | 2210.01006 | null | https://arxiv.org/abs/2210.01006v1 | https://arxiv.org/pdf/2210.01006v1.pdf | Neural network for determining an asteroid mineral composition from reflectance spectra | Chemical and mineral compositions of asteroids reflect the formation and history of our Solar System. This knowledge is also important for planetary defence and in-space resource utilisation. We aim to develop a fast and robust neural-network-based method for deriving the mineral modal and chemical compositions of silicate materials from their visible and near-infrared spectra. The method should be able to process raw spectra without significant pre-processing. We designed a convolutional neural network with two hidden layers for the analysis of the spectra, and trained it using labelled reflectance spectra. For the training, we used a dataset that consisted of reflectance spectra of real silicate samples stored in the RELAB and C-Tape databases, namely olivine, orthopyroxene, clinopyroxene, their mixtures, and olivine-pyroxene-rich meteorites. We used the model on two datasets. First, we evaluated the model reliability on a test dataset where we compared the model classification with known compositional reference values. The individual classification results are mostly within 10 percentage-point intervals around the correct values. Second, we classified the reflectance spectra of S-complex (Q-type and V-type, also including A-type) asteroids with known Bus-DeMeo taxonomy classes. The predicted mineral chemical composition of S-type and Q-type asteroids agree with the chemical composition of ordinary chondrites. The modal abundances of V-type and A-type asteroids show a dominant contribution of orthopyroxene and olivine, respectively. Additionally, our predictions of the mineral modal composition of S-type and Q-type asteroids show an apparent depletion of olivine related to the attenuation of its diagnostic absorptions with space weathering. This trend is consistent with previous results of the slower pyroxene response to space weathering relative to olivine. | ['Tomáš Kohout', 'Arto Klami', 'Antti Penttilä', 'David Korda'] | 2022-10-03 | null | null | null | null | ['type'] | ['speech'] | [-1.27380164e-02 -1.76449776e-01 5.57864942e-02 -1.44955099e-01
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c7781018-96fb-4afd-9f9c-5d338b56fde1 | sunet-a-deep-learning-architecture-for-acute | 1810.13304 | null | http://arxiv.org/abs/1810.13304v2 | http://arxiv.org/pdf/1810.13304v2.pdf | Acute and sub-acute stroke lesion segmentation from multimodal MRI | Acute stroke lesion segmentation tasks are of great clinical interest as they
can help doctors make better informed treatment decisions. Magnetic resonance
imaging (MRI) is time demanding but can provide images that are considered gold
standard for diagnosis. Automated stroke lesion segmentation can provide with
an estimate of the location and volume of the lesioned tissue, which can help
in the clinical practice to better assess and evaluate the risks of each
treatment. We propose a deep learning methodology for acute and sub-acute
stroke lesion segmentation using multimodal MR imaging. The proposed method is
evaluated using two public datasets from the 2015 Ischemic Stroke Lesion
Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute
stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES)
from diffusion, perfusion and anatomical MRI modalities. The performance is
compared against state-of-the-art methods with a blind online testing set
evaluation on each of the challenges. At the time of submitting this
manuscript, our approach is the first method in the online rankings for the
SISS (DSC=0.59$\pm$0.31) and SPES sub-tasks (DSC=0.84$\pm$0.10). When compared
with the rest of submitted strategies, we achieve top rank performance with a
lower Hausdorff distance. Better segmentation results are obtained by
leveraging the anatomy and pathophysiology of acute stroke lesions and using a
combined approach to minimize the effects of class imbalance. The same training
procedure is used for both tasks, showing the proposed methodology can
generalize well enough to deal with different unrelated tasks and imaging
modalities without training hyper-parameter tuning. A public version of the
proposed method has been released to the scientific community at
https://github.com/NIC-VICOROB/stroke-mri-segmentation. | ['Xavier Lladó', 'Arnau Oliver', 'Albert Clèrigues', 'Jose Bernal', 'Sergi Valverde', 'Jordi Freixenet'] | 2018-10-31 | null | null | null | null | ['acute-stroke-lesion-segmentation', 'ischemic-stroke-lesion-segmentation', 'outcome-prediction-in-multimodal-mri'] | ['medical', 'medical', 'medical'] | [ 3.63518782e-02 -8.01870599e-02 -2.73459643e-01 -3.16897660e-01
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dffda702-c692-48d6-8755-f7c6ad0ada45 | upgraded-w-net-with-attention-gates-and-its | 2011.10654 | null | https://arxiv.org/abs/2011.10654v1 | https://arxiv.org/pdf/2011.10654v1.pdf | Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation | Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth. A possible solution for the problem would be an unsupervised deep learning based approach for automated segmentation, which this research work tries to address. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. In addition, to suppress noise in the segmentation we added attention gates to the skip connections. The loss for the segmentation output was calculated using soft N-Cuts and for the reconstruction output using SSIM. Conditional Random Fields were used as a post-processing step to fine-tune the results. The proposed method has shown promising results, with a dice coefficient of 0.88 for the liver segmentation compared against manual segmentation. | ['Andreas Nürnberger', 'Oliver Speck', 'Soumick Chatterjee', 'Dhanunjaya Mitta'] | 2020-11-20 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.91607371e-01 4.19911623e-01 1.56258255e-01 -7.26804614e-01
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ce98b2d1-2cc1-430c-9612-44e32a8b051c | exploiting-out-of-domain-parallel-data | 1907.03060 | null | https://arxiv.org/abs/1907.03060v1 | https://arxiv.org/pdf/1907.03060v1.pdf | Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation | This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking. Although there are many solutions for low-resource scenarios, such as multilingual NMT and back-translation, we have empirically confirmed their limited success when restricted to in-domain data. We therefore propose to exploit out-of-domain data through transfer learning, by using it to first train a multilingual NMT model followed by multistage fine-tuning on in-domain parallel and back-translated pseudo-parallel data. Our approach, which combines domain adaptation, multilingualism, and back-translation, helps improve the translation quality by more than 3.7 BLEU points, over a strong baseline, for this extremely low-resource scenario. | ['Kenji Imamura', 'Raj Dabre', 'Atsushi Fujita', 'Aizhan Imankulova'] | 2019-07-06 | exploiting-out-of-domain-parallel-data-1 | https://aclanthology.org/W19-6613 | https://aclanthology.org/W19-6613.pdf | ws-2019-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 9.36678499e-02 -1.16999604e-01 -3.96218628e-01 -3.21874470e-01
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5ea66e50-d574-40ed-b0bc-42366bc1c287 | does-partial-pretranslation-can-improve-low | null | null | https://aclanthology.org/2022.wat-1.10 | https://aclanthology.org/2022.wat-1.10.pdf | Does partial pretranslation can improve low ressourced-languages pairs? | We study the effects of a local and punctual pretranslation of the source corpus on the performance of a Transformer translation model. The pretranslations are performed at the morphological (morpheme translation), lexical (word translation) and morphosyntactic (numeral groups and dates) levels. We focus on small and medium-sized training corpora (50K ~ 2.5M bisegments) and on a linguistically distant language pair (Japanese and French). We find that this type of pretranslation does not lead to significant progress. We describe the motivations of the approach, the specific difficulties of Japanese-French translation. We discuss the possible reasons for the observed underperformance. | ['Raoul Blin'] | null | null | null | null | wat-2022-10 | ['word-translation'] | ['natural-language-processing'] | [ 1.33864000e-01 -1.60577208e-01 -2.48948410e-01 -3.38374227e-01
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1840e4c8-dc71-4c93-8a30-024e403c5b36 | dag-recurrent-neural-networks-for-scene | 1509.00552 | null | http://arxiv.org/abs/1509.00552v2 | http://arxiv.org/pdf/1509.00552v2.pdf | DAG-Recurrent Neural Networks For Scene Labeling | In image labeling, local representations for image units are usually
generated from their surrounding image patches, thus long-range contextual
information is not effectively encoded. In this paper, we introduce recurrent
neural networks (RNNs) to address this issue. Specifically, directed acyclic
graph RNNs (DAG-RNNs) are proposed to process DAG-structured images, which
enables the network to model long-range semantic dependencies among image
units. Our DAG-RNNs are capable of tremendously enhancing the discriminative
power of local representations, which significantly benefits the local
classification. Meanwhile, we propose a novel class weighting function that
attends to rare classes, which phenomenally boosts the recognition accuracy for
non-frequent classes. Integrating with convolution and deconvolution layers,
our DAG-RNNs achieve new state-of-the-art results on the challenging SiftFlow,
CamVid and Barcelona benchmarks. | ['Bing Wang', 'Gang Wang', 'Zhen Zuo', 'Bing Shuai'] | 2015-09-02 | dag-recurrent-neural-networks-for-scene-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Shuai_DAG-Recurrent_Neural_Networks_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Shuai_DAG-Recurrent_Neural_Networks_CVPR_2016_paper.pdf | cvpr-2016-6 | ['scene-labeling'] | ['computer-vision'] | [ 1.77077681e-01 -1.51303828e-01 -5.11503577e-01 -6.54942036e-01
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3880ccfb-7dfd-47a6-ac42-8c3e4c538d0e | a-methodology-of-weed-crop-classification | 2010.14708 | null | https://arxiv.org/abs/2010.14708v2 | https://arxiv.org/pdf/2010.14708v2.pdf | Crop and weed classification based on AutoML | CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19. | ['Meiyu Jiang', 'Soheila Garshasbi', 'Qingguo Zhou', 'Jun Shen', 'XueTao Jiang', 'BinBin Yong'] | 2020-10-28 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.72876477e-01 -1.04354329e-01 -2.36047298e-01 5.18455990e-02
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3cb2ca47-e32c-4dc4-aae4-2f907f579b72 | image-deblurring-by-exploring-in-depth | 2303.15198 | null | https://arxiv.org/abs/2303.15198v1 | https://arxiv.org/pdf/2303.15198v1.pdf | Image Deblurring by Exploring In-depth Properties of Transformer | Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of transformer properties, we introduce the pretrained transformers to address this problem. In particular, we leverage deep features extracted from a pretrained vision transformer (ViT) to encourage recovered images to be sharp without sacrificing the performance measured by the quantitative metrics. The pretrained transformer can capture the global topological relations (i.e., self-similarity) of image, and we observe that the captured topological relations about the sharp image will change when blur occurs. By comparing the transformer features between recovered image and target one, the pretrained transformer provides high-resolution blur-sensitive semantic information, which is critical in measuring the sharpness of the deblurred image. On the basis of the advantages, we present two types of novel perceptual losses to guide image deblurring. One regards the features as vectors and computes the discrepancy between representations extracted from recovered image and target one in Euclidean space. The other type considers the features extracted from an image as a distribution and compares the distribution discrepancy between recovered image and target one. We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks. | ['Jiayi Ma', 'Xianming Liu', 'Junjun Jiang', 'Pengwei Liang'] | 2023-03-24 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 3.40807140e-01 -3.52883220e-01 2.13653013e-01 -1.43113919e-02
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ee7db93d-628c-4f72-a2fa-be9a7da2448e | masking-orchestration-multi-task-pretraining | 2003.04994 | null | https://arxiv.org/abs/2003.04994v1 | https://arxiv.org/pdf/2003.04994v1.pdf | Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning | Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders. | ['Yating Zhang', 'Tianyi Wang', 'Xiaozhong Liu', 'Qiong Zhang', 'Changlong Sun'] | 2020-02-27 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 7.15988636e-01 5.63483596e-01 -2.23232299e-01 -5.20137310e-01
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1324f7b3-be59-44ac-a919-3a4982413e90 | deepproblog-neural-probabilistic-logic-2 | 1907.08194 | null | https://arxiv.org/abs/1907.08194v2 | https://arxiv.org/pdf/1907.08194v2.pdf | Neural Probabilistic Logic Programming in DeepProbLog | We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples. | ['Thomas Demeester', 'Sebastijan Dumančić', 'Luc De Raedt', 'Angelika Kimmig', 'Robin Manhaeve'] | 2019-07-18 | null | null | null | neurips-2018-12 | ['program-induction'] | ['computer-code'] | [-3.63748558e-02 5.63023388e-01 -5.94107568e-01 -7.27886975e-01
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c80ad223-08ad-4073-9d3a-5cbd9b0a596b | speech-vgg-a-deep-feature-extractor-for | 1910.09909 | null | https://arxiv.org/abs/1910.09909v5 | https://arxiv.org/pdf/1910.09909v5.pdf | Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing | Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to date, most of these approaches are task-specific and designed for within-task transfer learning between different datasets or setups of a particular task. In turn, learning task-independent representation of speech and cross-task applications of transfer learning remain less common. Here, we introduce an encoder capturing word-level representations of speech for cross-task transfer learning. We demonstrate the application of the pre-trained encoder in four distinct speech and audio processing tasks: (i) speech enhancement, (ii) language identification, (iii) speech, noise, and music classification, and (iv) speaker identification. In each task, we compare the performance of our cross-task transfer learning approach to task-specific baselines. Our results show that the speech representation captured by the encoder through the pre-training is transferable across distinct speech processing tasks and datasets. Notably, even simple applications of our pre-trained encoder outperformed task-specific methods, or were comparable, depending on the task. | ['Milos Cernak', 'Pierre Beckmann', 'Mikolaj Kegler'] | 2019-10-22 | null | null | null | null | ['music-classification'] | ['music'] | [ 6.59657478e-01 4.45431247e-02 -2.37962212e-02 -6.06482744e-01
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8ac0ab7e-25b0-43ff-bec4-bd5d8469e17c | warm-start-actor-critic-from-approximation | 2306.11271 | null | https://arxiv.org/abs/2306.11271v1 | https://arxiv.org/pdf/2306.11271v1.pdf | Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap | Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical studies have demonstrated that the performance of Warm-Start RL can be improved \textit{quickly} in some cases but become \textit{stagnant} in other cases, especially when the function approximation is used. To this end, the primary objective of this work is to build a fundamental understanding on ``\textit{whether and when online learning can be significantly accelerated by a warm-start policy from offline RL?}''. Specifically, we consider the widely used Actor-Critic (A-C) method with a prior policy. We first quantify the approximation errors in the Actor update and the Critic update, respectively. Next, we cast the Warm-Start A-C algorithm as Newton's method with perturbation, and study the impact of the approximation errors on the finite-time learning performance with inaccurate Actor/Critic updates. Under some general technical conditions, we derive the upper bounds, which shed light on achieving the desired finite-learning performance in the Warm-Start A-C algorithm. In particular, our findings reveal that it is essential to reduce the algorithm bias in online learning. We also obtain lower bounds on the sub-optimality gap of the Warm-Start A-C algorithm to quantify the impact of the bias and error propagation. | ['Junshan Zhang', 'Sen Lin', 'Hang Wang'] | 2023-06-20 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.17938319e-01 5.52976616e-02 -4.48289335e-01 1.32792130e-01
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9bc55ffd-8f8f-4511-91dc-6dba3fa038ff | segment-level-neural-conditional-random | null | null | https://aclanthology.org/I17-2017 | https://aclanthology.org/I17-2017.pdf | Segment-Level Neural Conditional Random Fields for Named Entity Recognition | We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks. | ['Ikuya Yamada', 'Motoki Sato', 'Yuji Matsumoto', 'Hiroyuki Shindo'] | 2017-11-01 | segment-level-neural-conditional-random-1 | https://aclanthology.org/I17-2017 | https://aclanthology.org/I17-2017.pdf | ijcnlp-2017-11 | ['morphological-tagging'] | ['natural-language-processing'] | [ 3.08703780e-02 5.28114676e-01 -7.04711616e-01 -6.18890285e-01
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-1.05961487e-01 4.83873440e-03 4.02991086e-01 1.06146038e+00
-8.58758330e-01 -1.81378472e+00 -1.49634853e-01 4.31093812e-01
-1.08251297e+00 -7.62954295e-01 -2.94925794e-02 4.72393483e-01
1.61138713e-01 9.56311226e-01 2.76690662e-01 -2.66849250e-01
-9.50585455e-02 4.93045092e-01 2.42068365e-01 -1.22850823e+00
-1.06042051e+00 1.01238109e-01 6.24664426e-01 -3.93815786e-01
-7.75715590e-01 -4.71426755e-01 -1.88708162e+00 1.00841485e-01
-6.09345138e-01 7.50652671e-01 6.43876851e-01 1.42008054e+00
2.24039838e-01 1.11075796e-01 2.50641465e-01 -8.10845971e-01
-3.13105807e-02 -1.34817100e+00 -7.44085371e-01 -2.36122832e-02
1.62460804e-01 -1.33494064e-01 1.50764540e-01 4.50768262e-01] | [9.998666763305664, 9.734158515930176] |
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