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This study was built on PyTorch version 1.8.1https://pytorch.org/, and using a PC with an Nvidia GTX 1080 GPU. We trained the feature extractors using SGD optimizer {{cite:ab7d9be639266ee899dc641eb1551d973da87da8}} with a momentum of 0.9 and cosine annealing learning rate {{cite:f46da79ede090d1561bb34861210db0b0518af16... | m | 455104e109bffd3bdf4fec55f1b369a0 |
We solve these equations with the renormalized Numerov propagator method
{{cite:8c3e5ea7c02dcb39dbb0ce25b396cca0c4d84d1d}}, {{cite:3680f278fc6c27fc817884008e4b2e676d5482b7}}. This method implies that one defines an
equidistant grid {{formula:bd87caeb-af36-4a5a-8541-656b7da80442}} and propagates the matrix {{formula:3c... | m | cf764c3c9b521a5d4b52e2ad910c874a |
The presented dataset augmentation technique was not compared directly to other strategies due to a lack of readily available alternatives. Through a literature search, shown in Appendix 1REF , it was found that only a single paper was published which performed dataset augmentation on structural connectomics data, spec... | d | 7a413c4588b44557fa7350d4ab1aa6c4 |
On the level of {{formula:ef735177-0a3a-4959-ad5a-ecd9cdf25fc4}} -modules, we will use {{formula:6a97056c-f899-4ba9-9685-897f39a9e8f4}} and {{formula:f918799a-ba0f-47d8-9284-c74bd3e62512}}
for the usual and proper direct image functors, and {{formula:6594f7df-8fea-4183-ace2-f024c13ecab6}} and
{{formula:6f8ebd41-b4b5... | r | 7fe385a5176dffc01c064052fdcb8e05 |
According to {{cite:6196a41f019ce689aeb9fba653ccc5b06243c3eb}}, we can upgrade Prokhorov's theorem to be sequentially compact with the Wasserstein 2-metric if
{{formula:e5049e56-3e45-43cd-aceb-4b080976eb43}}
| r | 530f6d04fea58fc53c9dcb80d2a59043 |
We can obtain a similar result by the technique of completing the square. Since we know that a scale and shift on a function argument does not change the function shape, i.e. scaling and shifting a Gaussian will give a Gaussian curve, we can use the technique of completing the square to recognize the mean and covarianc... | m | a9b7627f2307cfdfd0b88f44e2ee2c2b |
The proximity effect in superconductor (S) – ferromagnet (F) hybrid structures is known to be responsible for rich variety of exciting interference phenomena affecting thermodynamic and transport properties of these systems {{cite:717610e1508331b434cc810fad2964420b47ffff}}, {{cite:1fd3b46431dbb62bf6237654c44b21eaf93032... | i | 59ad6f89ecfe1f1cc59e496d63809e67 |
Robustness to adversarial distortions.
We extend our analysis on the robustness of CNN-GRU and LipForensics (two of the baselines for temporal DF detection methods {{cite:dad2e609e482c34cede37ba91be426624a2b67ec}}, {{cite:01b887e7f159c9f7c49caca77e3018bd2c1d64a7}}) to black box attacks {{cite:ec7a206d706f0aee5dcc1574a0... | r | e1b0f8055681a45dcf14fd731f87c94a |
where {{formula:40b21f20-6a17-4e3c-92a3-27788d281327}} is the medium gluon number density.
A great amount of abundant experimental data {{cite:d822825cb6a3c51ac8c88fca052f5b335b8caeb1}}, {{cite:a98e136dc11e2d61d11fc16cadc48a30b8d21c5e}}, {{cite:a96fcf79aa893f500d19a75d01ec3d6f66f98545}}, {{cite:c83521f87d3eb5673e8bd06... | i | 07b718692ac926067a7bd42187d8bc16 |
Inspired by the collaborative training scheme proposed by {{cite:98b593d5d52270cd730f7bf283a9bd1891a65376}}, {{cite:e1aab8d7bec4a8127b40a523ad60275117c85d13}}, a co-training network with a noisy label filter has been utilized to more explicitly enhance the network robustness and greatly alleviate the selection bias, as... | d | 5de5d6b0b684af894338e322cd0ad7db |
To address this limitation, recent research has started to focus on generalization approaches for RL.
Ideally, these general agents should be capable of zero-shot transfer to previously unseen environments and to changes in the problem setting while interacting with an environment {{cite:771107d3d53162e5ebc0a7357ef8dd8... | i | 6787413cb722d6ef7eeab35590803a3e |
In addition, all models were trained with the Adam optimiser {{cite:605a6dadaf74fb9de84ce556608c25aa388215e1}}, through the weight-decay wrapper provided by Tensorflow-Addons in order to implement weight-decay regularisation compatible with Adam{{cite:012fdc2a55292cb1a8c947c7ba1d1063e70f41e6}}. The value of the weight-... | m | 925eb0e2661fb7f093136cc378f9f80a |
User Study. We undertake a user study comparable to {{cite:a7890fe3d4526c32e1f35eac1c503277dc0bd809}} to dig deeper into the six approaches: AdaIN {{cite:c4884572e8455d2d23064da1c91c270793ae5c24}}, SAFIN {{cite:a7890fe3d4526c32e1f35eac1c503277dc0bd809}}, AdaAttN {{cite:abccb846746b6c8105d75a653269baa77ff7d236}}, LST {{... | m | 1b6ae9a458f104c90b3230539e5c1206 |
Finally, we have reached to our prefixed destination, but this is not our final destination because the model runs throughout the assumption on unchanged population size over time. However, evolutionary dynamics and ecological process, those
are dependent on frequency dependent selection and demographic fluctuations, s... | d | d914fc4043f35acff33701903e15e742 |
The InPTA experiment aims to use the unique strengths of the Giant Metrewave Radio Telescope {{cite:f7b572c8bb8e94d235cf0f215171627cd8cb2d9c}} after its recent major upgrade {{cite:4ae0fe74e8244233c6ea1262929838f347fef40c}} to complement the international PTA efforts by providing a unique low-frequency view of PTA puls... | i | 14748c76bb21df4679f1ca1a89563c12 |
These findings have later been leveraged to provide powerful unsupervised computer vision algorithms {{cite:477dc7990396240529421e6566b0222f3ee6c006}}, {{cite:bef4f749423d8c2dac18e70ad7df26ec03ad0d09}}, {{cite:e882aa5d4b04453a822055bfb9bf10a08e41b59e}}. When applied to images, these pioneering techniques usually requir... | i | 6a03a0ba997ed49b1ae69891e689d33a |
We believe that the practical applicability of BERT-based re-ranking models is currently limited to offline scoring or domains where users are willing to accept multiple second delays in their search workflow. Future work will likely focus on the gap between the contextualized and non-contextualized models – both in te... | r | 1ee20250b90f4f9822ffe684ff50a7fc |
Our understanding of the link from scattering amplitudes to classical solutions has matured. It has become straightforward
to construct the linearised solutions associated to massive three-point amplitudes in four dimensions.
The technical developments which have clarified this link are the KMOC formalism {{cite:96f0e2... | d | 77b85e61172020ac47f2ae88ea17aad4 |
An important aspect to consider in analyzing the results is that the individuals' movement patterns seem unique. This is not new, and several papers have exempted the individuality of human walking and running {{cite:4f2d658222f522f556178857258d225691176063}}. In this sense, since the biomechanical patterns are probabl... | d | 3ce129c8f8ac0410d29cdba48b9bf8ce |
Finally, we have only considered finite-dimensional parametric sampling score and outcome models. Recent advances in the machine learning literature have expanded the tool box for causal inference. In particular, TMLE {{cite:a1f197988a6daa5a9473819dfc7baf26bdbe3ba6}}, {{cite:749a22ebbbe580907f73504c3770054cc2fb2460}} a... | d | 0098e5b585ceb666cfe2c0000dc31571 |
Due to the emphasis on the symmetry, quantization of gauge field theories are usually performed in the Lagrangian formalism, rather than in the Hamiltonian formalism. The standard procedure for fixing the gauge is the Faddeev-Popov method {{cite:66ba4cdc7593dec2bdaf9ea5589d059f90bae648}}, together with its associated B... | i | efa29f87284c976d1309923ffb999f75 |
The determinant quantum Monte Carlo method {{cite:8e0ab257b834679a91814733aca860191c7e4204}}, {{cite:d984e64c4dc4c31672773efba9e4d74c18973095}}, {{cite:7e3b9d1916cd131268f984119f321c660e0f354b}}, {{cite:ae2911fed1f7984e959ee64b2accafd63285efbd}} is a powerful technique to find equilibrium properties of interacting ferm... | r | 9ca47b6722fe0d45b2e96a45b103fed8 |
Training and evaluation.
As in {{cite:76f6e0dadd7844daeb854edf5ba4a5c31e66dc65}}, we use a {{formula:9832180c-49c9-45e4-93b4-0ae95144d1f7}} -wide {{cite:4529d9bbbdcf4b42c3a1ef11d14836a3ccf4afa2}}, selective-kernel {{cite:ae5e3c11fed5a20f79603916015f2c7fb21ff3f7}} ResNet50-D {{cite:4f74d7a8579f02a6191c807ae87de7ecb4c56e... | r | afb3c9f1388c20dfea546dad93c073b5 |
For the purpose of generating plots, we choose {{formula:547e5ef3-ec3b-4451-9363-f1c76df89200}} and {{formula:592e5ed1-067f-4add-9780-4a70dea0686c}} as obtained by matching MIS with the holographic description of {{formula:ad3ebdde-95c8-4801-bda7-ab611b755217}} SYM theory at infinite 't Hooft coupling and for infini... | r | d3b902bbd4139a948039a1ae411a8467 |
Let {{formula:19fec2e3-4f35-459d-9991-4029cbba1d3e}} be a transcendental meromorphic function such that it has either at least two poles or exactly one pole which is not an omitted value. For such functions, there are infinitely many points whose iterated forward image is infinity, the only essential singularity of th... | i | 9b4457ab078f9d2b2e0f52f37696cc2f |
Despite their state-of-the-art performance on object classification tasks, deep neural networks (DNN) are highly prone to shortcut learning {{cite:e49b61ca4471b7f8d1a6b2584c5883ac4d98b9cb}}, {{cite:e5a53f651c1ad0c5525e71ec53fc7cb5348e63a7}}, {{cite:a30a362b5155ecd29dcfa99efcd1d4e6bb2d6e7d}}.
Instead of learning holisti... | i | 10f81446ed6ead2b86377987e148befc |
One may also consider the related concept of local rigidity, where there must exist only a finite number of realisations (up to isometric transformations) in the given normed space. This has also been studied intensively for many years in the Euclidean case (see, for example, {{cite:969b24e46b949372d86551f3691adbf9270d... | i | f95075a7e1456ad92d066de8d5c463e9 |
This effect that, as mentioned, has been largely unnoticed does not
have any implications for interferometric experiments
such as LIGO or Virgo {{cite:14c988eb25dfdb97cc14c81d389f2b4cd60e133b}}, {{cite:04241712a772bb118d8b494f9f0a13b442a6468d}}, {{cite:67dac592e55f142e0b622f7278e57933875a9bc8}} that depend only on the
... | i | 6912d8354f578f23f01ed1dba1ee61f5 |
With the help of these exchange interactions, we have calculated the ferromagnetic transition temperature (T{{formula:4f22ffe8-157c-40cd-bf3c-0b3ad66c5dea}} ) using a classical Monte Carlo (MC){{cite:7436bf3c36c5455e02134c0991117f02f4277dda}} method.
In this technique, we first set up the magnetic lattice. In SFRO the ... | m | 075a1b9548803e8bdfcaf2cdd2d2a8c9 |
The appearance of this emerging local scale symmetry has two consequences. First, it implies that the NR action leads to one equation of motion less than its relativistic counterpart. It turns out that the missing equation of motion is important, as it corresponds to the analog of the Poisson equation of NR gravity. We... | i | fe58b7f4290d4dfa613da810bb0cd931 |
We extended the calculations by using the covariant (relativistic) density functionals (CDF). The widely used parameter sets,
nonlinear parameter sets NL1, 2 {{cite:5b48b53a43cb0f25f072d57721f4a09ecf00de16}}, {{cite:2e1d23664e7bd4a5b922bb6913c4a1e490e9aec3}}, density-dependent RMF (DDRMF) parameter sets DD-ME1, 2, PKDD... | r | f681ff930d2a99fa726d92b2243eeb87 |
Most of the previous works on QG focus on generating the SQuAD-style single-hop question, which is relevant to one fact obtainable from a single sentence. Recently, there has been a surge of interest in QG for more complex multi-hop question generation, such as HotpotQA-style questions {{cite:4f74bc4adce950af210a96f57... | i | 12d237db4a73b876a91f3243173181d3 |
We present a simple and intuitive approach to semi-supervised learning on (potentially) infinite streams of unlabeled data. Our approach integrates insights from different bodies of work including self-training {{cite:c08f557afac206c3dd65325c05723c8b922052f1}}, {{cite:0b0e3f5448ddddfd825bf035de7b4572d66a62c5}}, pseudo-... | d | 8af61baaff39a6f89fd494b18094cd62 |
In the following, we will start by a summary of the canonical quantization of the free electromagnetic field in the Coulomb gauge using the LP field in position space and its equivalent in momentum space to make a link with the standard notations of the quantum optics literature {{cite:f42996eeef679d44a99bd2e137137f9f6... | i | d0b14998310511409b0ff726abdbe378 |
There is a range of GNN variants, that implement different aggregation strategies. In this section, we focus on standard graph convolutional networks (GCNs) {{cite:083cb01c53f4bdc69615b2ef023b51bc7f262f2d}} and graph attention networks (GATs) {{cite:a25387635eddaebbc53f361386354e00002506d9}}, since they are the core of... | m | 75f9d4a6c9de94bcca4545dbc1a14f03 |
There have recently been attempts to test the idea of braneworld against
observations {{cite:666f400e688405edea917296c9094df6ac068e6f}}, {{cite:dde3eefb594717f80a0f1d414807a8bf0903558c}}, {{cite:07a00396f5cd5fae63bfab3ad4a1c19e30c7351c}}, {{cite:9302670b86b5f5dba824578802b9d5b0b11abfb0}}, {{cite:f285503c9fccff064121ae7... | d | 9b0a96d66fdc74f711374c9823d44d66 |
In the prototype, we represent each clause in a proof state only by its size and order number, applying a logistic regression as a Q-value function. We will need an elaborate feature extraction procedure to complete this oversimplified model to a competitive ATP. We plan to use graph neural networks similar to those us... | d | cd92d5760e1d139ef45c3019c867524e |
Although no available measurement of the {{formula:248df951-4c0a-4e36-a5fc-d55717a91fdd}} {{formula:40f0b04b-85bb-45a2-8911-1953501f592a}}
{{formula:41790225-694f-47e9-9a63-54208d801308}} decays is enumerated by PDG so far {{cite:5d94e8627021c7cfc48745b6488cf9e77982c316}},
there is renewed experimental interest in {... | d | f2931a0b71faaf4766e3bbd711924103 |
Cloud computing has allowed massive data collection from users, forming Big Data for machine learning in 2010s {{cite:7f0936323592d86706432cf8ee4f03af66b4c7aa}}, {{cite:51bc08de429ba9e86288302a37a374c074a78d61}}. However, because of the privacy concerns, such as (1) utilization of them other than the original intent or... | i | 779c8bc1abda9636d01b06368ca08c85 |
Humans possess a remarkable ability to parse images simply by looking at them.
In a blink of an eye, a human can fully analyze an image and separate all its components.
People can perform several tasks simultaneously by analyzing an image, e.g., object detection and contour detection.
Furthermore, humans can easily gen... | i | 0531d7503be7ac01a428b1d1787eb726 |
Hioki and Maeda {{cite:2c9d34e3284c0a939b0baece51415f4e556fca9f}} characterized the distortion and shadow size by proposing the two observables, viz. {{formula:e1280375-fdd3-4a9c-963c-f98e6b4cd9cd}} and {{formula:55749970-ad0e-4d14-b974-3babed9d2634}} . They approximate the shadow by a perfect reference circle, with {... | m | 2d7982df85a8f96dd67d4e51a7f7164d |
Our results in this paper apply to de Sitter geometries and inflation, at tree-level. Indeed, our MLT and the ansatz we employ for our partial-energy shifts follow from de Sitter mode functions. It would be interesting to extend our results to other accelerating FLRW spacetimes, with help from the recent generalisation... | i | 39adb20901d05e305aaa8178bdb0c51d |
Other approaches, such as GradCAM {{cite:13f4d355ef4f868bea059c165ae8b6eb7076e9e0}}, calculate gradients by back-propagating the prediction score through the target layer of the network and apply them as weights to combine the forward feature maps. These methods are generally faster than RISE since they only require a ... | i | ed2ac053af6191d5be364f71596fb02a |
(1) First, we encode relation instances in {{formula:74bcb6ad-6b75-4fc9-8f7c-c0d90664c8d9}} and {{formula:a1261e69-7d85-4fa4-a592-724b661f0c42}} using the entity pair encoder implemented as the pretrained BERT {{cite:6b937ffe94ef068ad218e2add20d2b352068eb88}}, which takes relation instances {{formula:4b70d439-d4b2-4e... | m | 3597c6968ae185f4dd72d92687756016 |
Transfer-MAML The transfer meta-learning model introduced in {{cite:21abd44978d2e4feff54fb5b8b10df358e7b547b}}, which learns a global model on the Train S-Set, and then transfers the global weights to learn the MAML.
| m | 0649a3509de871025bf34843960263f3 |
We conclude this section with a few remarks on the above complexity results of UCGS.
First, we note that UCGS is similar to FGM in {{cite:2de56db4e44f4d557265a20536b6bb622dce010e}} in the sense that the number of gradient evaluations generalizes the accelearated gradient descent method in {{cite:18a2b03a1384328e0133b6d... | m | cfe0bc10231d8f8db227b829661d6fb3 |
where where each {{formula:0842ba7c-22f4-4614-890a-52294401291e}} represents the oscillations of the function at a different length scale, and our tensor {{formula:7c44131d-595a-4efc-8483-a514f93e6a4b}} explores correlations between different length scales. This implicit renormalization scheme is a discovery of the q... | d | 7fb5021fc07cca034ab6e4d61c9853f9 |
Datasets and Baselines:
Evaluating treatment effect estimation methods requires all potential outcomes to be available, which is impossible due to the fundamental problem of causal inference. Thus, following {{cite:c81e267652c27695e2b2a99605f7dd99edad04b2}}, {{cite:30e729f98d0eabb55a8513bcc6226463d6e7e7e4}}, {{cite:a9e... | r | d8d726b93ca6fa14ba2c8b9f1457274f |
Data Granularity.
For some coarse-grained tasks,
their pre-training dataset, e.g., the ImageNet{{cite:47ca2c2c8daaac4fe5e80f0bdac4e052add9ae32}} for image recognition,
usually has millions of images distributed over only thousands of classes.
This relatively dense distribution implies that a training batch may contain... | d | b8e04e72d9dac5d041a3423a56933c86 |
While dynamical systems theory is a natural language with which to frame DNN optimization, the complex dependence on optimizer, architecture, activation function, and training data has historically kept efforts in this direction to a minimum. This has led to a reliance on heuristic methods, such as iterative magnitude ... | d | deb77d56a1720986091441a1d39b2887 |
The nebular Heii{{formula:582c28e4-fbf5-415e-bf0d-df5b65f0e19f}} 4686 emission observed in some SF
galaxies has often been attributed to X-ray emission from HMXBs
{{cite:7127c6c855fd032d6aa790c512fe6a8fba498f3d}}, {{cite:24538526f605df8c2fb4d0621ad068ccb3959d4b}}. This hypothesis has been critically
tested by {{cite:4b... | d | fe409b3692bbb3f48e69cdefd0ac7549 |
In this section we review the classical definitions, algorithms and convergence results from convex optimization (mostly referring to {{cite:3a7df28d352f1f511eb3b0d79a299a87068d4f51}}, {{cite:20dd65c7183f158ef839af24ba92f766d9e3289f}}). As the scope of gradient methods in convex optimization is extremely large, we only... | m | d7839c987f656af56367bab4ff22b886 |
Finding Steiner trees and related network design problems were intensively studied in undirected graphs, directed graphs, and planar graphs, from the viewpoint of approximation and parameterized algorithms {{cite:54616d54670c08d22b66a51b7f5ecd06d465fddd}}, {{cite:0e7c7242b720350168dfc8c82f47c2866b3f1140}}, {{cite:3a345... | i | b735f3ac45f6bfdfd7e58bc07f8ab4f1 |
A variation over the previous iterator is the accumulator. Instead of directly applying the chosen gates to the target qubits, one defines an accumulator qubit, which is at state {{formula:517dc9ac-2b0f-4756-b1f1-904d19dba405}} until we control on the selected value of the indices, and stays {{formula:288e0e2e-65cc-41... | m | fecba2f2584b4dcb433daec818cdaaf1 |
given some desired {{formula:bbe3ae5c-fc51-4664-a09b-8f34109c3216}} . The ranks found for {{formula:a2dea5f9-2da9-4c83-983f-63ba208db9c6}} are shown in Figure REF .
The POD bases for {{formula:9a7bdec6-2b52-45bf-bcb4-f54854701832}} and {{formula:2593651d-de3b-497d-b38a-2afed5df24bb}} reach full rank {{formula:cc6c84... | r | d40cf50759257a312f22f09c5bbbf4dd |
In Section , we rigorously construct the (sequence of) statistical experiment(s) generated by the observation (REF ) under the dynamics (REF ) that we denote {{formula:904af4de-215c-42b7-a3fa-9fba96b008f1}} . It is well defined and regular in the classical sense of Ibragimov and Hasminski {{cite:d53d5e6c4a12f517b10f938... | r | 15cd6e9f64c1b2d28786208f9f02f504 |
In this paper, we introduced DETRtime, a novel time-series segmentation approach inspired by the recent success of the DETR architecture {{cite:7b2a96ea75b5aad8d58a65aa5dacc3c7a1256fea}}. Furthermore, we showed that deep learning models designed initially for image segmentation are also well-suited for EEG time-series ... | d | 3a4930105d8978bf67590615d4b0b6ca |
Our proposed cross-view affordance knowledge transfer framework for affordance grounding is shown in Fig. REF . During training, we first use Resnet50 {{cite:1d1e330f027052e1e5daef6b1d595a77d49612f8}} to extract the features of exocentric and egocentric images to obtain {{formula:c068ac60-4530-4066-89fd-560fa728876b}} ... | m | a60682c43e9ee2e3dc91e202ecc8f447 |
We thank S. Hilgenfeldt for helpful discussions over the course of this work.
The authors acknowledge support by the National Science Foundation under NSF CAREER Grant No. CBET-1846752 (MG) and by the Blue Waters project (OCI- 0725070, ACI- 1238993), a joint effort of the University of Illinois at Urbana-Champaign and ... | m | ffd172aee20f6c199ef239d9182d680a |
The other school is metapath-based approaches.
A metapath means composite relation connecting two objects at the network schema level.
It has been adopted to capture semantic information {{cite:d258c0d8950b0769288d8b4462cfd981ef70d9ea}}, {{cite:be8f6060f7f8cc703c5d82e339bb71ea4e521d52}}.
Taking the movie data in Fig. R... | i | 7dbbe2a193589083f2dc251ec6f9f914 |
Neural Timeseries to 2D: We divide the process of creating images from EEG signals into the following three steps.
Window Extraction: EEG time-series signals are extracted into windows of 2, 4, and 6 seconds. For example, if a signal contains 24 seconds, we get 12, 6, and, 4 no of windows respectively. Varying window... | m | 6f8571598f82a3ece93f3c224ca36b9f |
Weighted Boxes Fusion (WBF) {{cite:f80a07ccfa3adf876c8076497550006d6b24127d}} targets specifically at post-processing the bounding boxes from different models. Instead of selecting one best bounding box (NMS) or keeping all of the bounding boxes (Soft-NMS), WBF produces a weighted average of the bounding boxes in terms... | m | 059747e48d4d498c5fd3d41a5c159e23 |
Although there is a significant amount of literature on applied transfer learning procedures {{cite:f7b9cdc06cf62d4267e865f9527c860bdf785a71}}, {{cite:2af57572bf6773dc73392fa5a1b1305aacb91c5b}}, {{cite:a44daf6f539394945b1f07065f5c827cc0771c53}}, {{cite:21ee15239aa5afc148a8c1939640c89ea26ef750}}, the literature on rigor... | i | e6be79d449355c1b27852d095c48fc2b |
Some of the most recent state-of-the-art approaches to keyword detection consider the problem as a sequence-to-sequence generation task. The first research leveraging this tactic was proposed by {{cite:1e711859fc8209e3272746dda7d7b188f8a7b1c7}}, employing a generative model for keyword prediction with a recurrent encod... | m | 00cd2d6e043a2582af6e82ca8751a266 |
In REF , the first term is the data fidelity term, the second term acts as a regularizer and {{formula:7db3b052-2598-4b29-861f-47a5de25e0bb}} is a regularization parameter. {{formula:f3c2b7fc-9613-4efa-b0d8-36e07dc762a0}} extracts a patch of size {{formula:bbbc3635-0c68-4efb-94b9-bafee697d8e4}} from the location ind... | m | 7526671b2d030cdf541ffff3747673b0 |
With the advances of deep neural networks, many deep learning-based methods {{cite:8107055ed0889f935d056443a32f0ac22e53a7bc}}, {{cite:bdc75fee01254088fc3cbbcf77461640280c0dbc}}, {{cite:725adac0e3293a56c96cf7e95bc5b61843120824}}, {{cite:d8c6ef0b5c2edb720d5c5c085bca9d8b1dde1ca2}}, {{cite:411f80631cf291a002023e81a1b66f2e2... | i | fce5e2ff3155a77600f36736843fe973 |
We introduced the idea of GPs in surface terminology that are known in statistics and machine learning literature. In contrast to ARMA models, they extend linear processes into continuous domain and, hence, can model a wider range of rough surfaces. In sum, we showed that grinded and honed surfaces can be simulated by ... | d | 9399a3b94abfab49dc6f4a8a5154fa27 |
Of course, follow-up observations have placed much stronger constraints on
strictly periodic signals. {{cite:1c56faf12b3170a41e681144402676c92df7110b}} completely excludes strict
periodicity for periods less than 14 hours. Going further, {{cite:860b403fb304ea750e0db1876fde33a607193284}}
provides the tightest constraint... | d | 67395d59c791c2d2fabf9f35d7e12d23 |
Cluster initial conditions and {{formula:ff55cc6c-8066-48b8-8a06-c633c86d370d}} -body code.
The initial positions and velocities of the stars were sampled from an isotropic Plummer model{{cite:fee9f07f6cea19bf2b1feba029f7ec5cfc7ad3cb}}, truncated at 20 scale radii. Initial stellar masses were sampled from a Kroupa IMF{... | m | a3f972c1eaae6dfb5ded0487c88785eb |
This area started with the investigation of Edge Estimation problem by Dell and Lapinskas {{cite:5281b13df2ee397dd1e72c5e4bb5ca212c8e7be0}}, {{cite:63687d933413ff3d835f7eb4f1f4942f3d388457}} and Beame et al. {{cite:ca7aae3d5b46424e098baba83852a343372fdfe1}}, then Bhattacharya et al. {{cite:ded9050d320dc4b377b0c4ebbd718... | r | bb3c449bc57fdf94240beb955dd82a4c |
Ref. James:2019gi emphasized the significance of the ridge-crossing process, also observed here, by which the nucleus switches from the {{formula:8b5143f0-544e-4ebf-8eed-8af335cf154d}} channel to the {{formula:c7a8f311-62bf-49cd-913a-f0c57c3fb73f}} channel.
This process is a discontinuous phase transition that occurs... | d | 4990f044fdaf4b75490c36960c03f597 |
The effects included in this analysis were purely standard model Physics. More generally one would apply the techniques discussed here to verify the avenues of discovery for new Physics hidden in the GWB. We note that the numerical method utilized in this work can be straightforwardly generalized to non-standard therma... | d | d40e48ed2eb01c230b866a8f4a07d782 |
While the ROC-AUC improvements might seem minor, the immense body of work on oversampling tells a different story. SMOTE is widely used and all major random forest algorithms (Catboost{{cite:bac12c992555b8020f9e93194ac680a9d9f7f6bb}}, lightGBM{{cite:77163c001d0170b5363477b438f8f1083a468268}} and XGBoost{{cite:fc4d72dcd... | d | a9642f80bb2d039f42bc1c2647de390d |
Ablation Studies.
(a) Spatial Benchmark:
We first present an ablation study on individual queries. The aim of this study
is to measure the error in relative to the variability of query latency. For
initial training of the latency prediction model, we used plans from spatial
benchmark {{cite:1b56c96c387a81de4d38b4960088... | r | 5d6391c2f69a54862a790aa0ab73fc61 |
paragraph4
.5em plus1ex minus.2ex-.5emReduce training noise in BN.
Apart from reducing train-test inconsistencies, several other normalization methods
attempt to reduce training noise of BatchNorm.
BatchRenorm (BRN) {{cite:86b41205e1432bd56ebbfecbf27d9acb94931be1}} introduces correction terms
to bring the training-time... | d | 3c439fa5eb90bf3b9c0e21134f40f0b7 |
Another issue of significant physical interest is the occurrence of invisibility, namely the scattering pattern is identically zero. Generically, it is believed that geometric singularities on the support of a generic medium scatterer prevents the occurrence of the invisibility phenomenon. We refer to {{cite:4a58028619... | d | 1b0e374cebe6bca9ec76ab3134467f57 |
In the CAMELYON16 challenge {{cite:e210911cbf9e9ae6d94534666614b178b6b6b6fe}}, hundreds of whole-slide images of sentinel lymph nodes sections with or without nodal metastases verified by standard immunohistochemical staining by expert pathologists are provided to the participants to build algorithms. The PCam data set... | r | 5801330464db2ea15fb28f4ab3b2486f |
1.11. For definitions not cited here, we refer the reader to {{cite:f513b855c8b62e0ae7d25d5e1e6912468340ab09}}, {{cite:11c23d059977056fff334c0a9551e71aec12f5e2}}, {{cite:eb56aa6610d444736d5162a5b5952e51c0b8ab4b}}, {{cite:ab1fbacc191fafb73fd4997c76a9ca50abfb82bd}}, {{cite:a96cde60cfc7288516b8c055c2d75840a166fecf}}, {{ci... | r | d25ecd5f385902b93b4fc2343fe4f9b0 |
We compare our method with the original styleGAN {{cite:121ee7b43d460f2e67ffe367d470861f6e2c6a14}} truncation to demonstrate the effectiveness of our approach both qualitatively and quantitatively.
| i | 120de683aae8d4e07d2b19150ef51bdb |
for which the positive singularity could be described by fundamental solution of fractional Laplacian.
As well as for the Laplacian, this method fails for the classification of isolated singularities for the Serrin's critical and supercritical case, i.e. {{formula:20d274b4-e86c-4bfc-bca0-bfb0f567b91d}} .
When {{formula... | i | 64e26806ef7bfd1988194e353761a8ef |
Since the objective of this paper is to propose a newer faster baseline, we must focus our scrutiny on three areas primarily, amount of resources used, the time required for the complete training process, and the final error percentage. In all the three fronts we either match the NASH or are better. Table REF gives a ... | r | 08c3b6cebdf9c0edd6c06440622abcfd |
We then apply our framework in [thm:thm:main]Theorem thm:thm:main to the sliding window model.
To that end, we first recall that given a {{formula:2a93eae1-4a23-41ce-a69f-e3bf7ba6604e}} -approximation algorithm for the insertion-only streaming model, the smooth histogram framework {{cite:bc163818ff33ac6117964f67a8b8e6c... | r | a7c0f5d3961b3cd9197dcc412bb4a30d |
Transfer learning (TL) based on pre-training and fine-tuning {{cite:bb09984b79fe396bc080aaab61e9f2e41bbef3b9}}, {{cite:4dbbb7864c6709af832c31f57d8a99bad6b5896a}}, {{cite:fddc555dd9c7b688960e6134b91198474db6266c}}, {{cite:d47827d81a9feec5087ac63611c1b3658f031a11}} is widely employed for domain adaptation tasks. Its basi... | i | 433fc7b1d1fa4fca1b60511f528a754f |
Recent computational approaches have attempted to infer participant beliefs by modeling their behavior {{cite:a33fb0612738cdd8c7b7a5929517b5add56f7a35}}, {{cite:3e2e290ac6d9c1663694597f2cf2ed66346747fe}}, {{cite:1c8026d0f80044e2b89d93cd6293cfd1deba719a}}, {{cite:60279aa9b5db32dfcec5bb5bda34354faf585c4e}}, {{cite:79912c... | i | 6df6945ff7a79e8b4b35c9c5c2098935 |
In this paper, we will conduct a systematic study of the {{formula:b13fc776-3f23-441c-be19-61746eed8a08}} distribution of {{formula:9cbe707c-3f82-4545-b9ff-58a8136616d9}} -meson in jets both in p+p and nucleus-nucleus collisions at the LHC energy. The initial {{formula:a4d121c5-ca49-4d48-ac85-6d599cd8578a}} distribut... | i | 9b21cffe6d71a1f29ce80e44d9267fcb |
In this paper, we show that properly designed and trained CNN codes with iterative decoders, which we call DeepIC, outperform the state-of-the-art by a significant margin on the challenging problem of communicating over two-user AWGN interference channels. While it had been shown in the literature {{cite:27cb2d5d5576b1... | d | 59ba2e217cafcb5314c295745e520ad6 |
In experiments, our dimerized NHQC may be realized in photonic systems.
The uniform part of hopping amplitude and quasiperiodic non-Hermitian
onsite potential could be realized by a frequency-modulated mode-locked
laser with gain medium, phase modulator and low-finesse intracavity
etalon, as proposed in Ref. {{cite:005... | d | 584fc6f52438b3d8f329ce6efd14a3a2 |
For us, the emphasis was to derive the first {{formula:41d16554-6b94-47c0-8190-d51045b4275b}} lower bound for the partially observed setting (Corollary REF ). Thus demonstrating that isotropic output noise does not necessarily help the learner and that such systems can be at least as hard; in fact, partial observabili... | d | 7d2677d1f75dc370f561da3197ce38de |
This result is interesting because central limit theorems for shape parameters of discrete random structures arising from computer science which have a variance which is significantly larger than the mean are rare; the only other example with a similar behavior for mean and variance can be found in Flajolet et al. {{ci... | i | a47bc2e35e5ba309174fa27c991b1731 |
Navigating nets.
In 2004 {{cite:40953bfc31a257b1ea5e0a7fa7666de83645ce96}} claimed that all {{formula:dfda4c8b-919e-43fa-8e27-68ba4634190f}} -nearest neighbors of a query point {{formula:85a05a5c-e1ed-44c7-9f3d-c5e4c1d0ac93}} are found by navigating nets in time {{formula:ee0ef287-93a5-4705-8c39-d83d81d0184f}} , where... | r | 4b0c835f4a3a5d066132f47bf91e7f27 |
The spin models are implemented with a system of two-component bosons in an optical lattice, which is well-described by the Bose-Hubbard model. These two states (lowest and second-lowest hyperfine states of {{formula:1f6681a3-0af2-45d1-b126-0afb29571634}} Li), labelled {{formula:0edc055e-64fb-4f02-a639-e7c2b68165c2}} ... | m | bebdc2494775435c3bf205b64cb32451 |
In our CRG setup see Fig. REF A, we use StyleGAN as generator {{formula:def09393-46a3-47b3-879a-c230e08d2f72}} and a two branch Resnet-101 based architecture as encoder {{formula:d39d8548-d278-44fd-8980-dbd0d8b93a90}} . We took inspiration from {{cite:701d25f343ae13467e2bc2f6bad20683670b76f7}} in designing the archite... | m | 6a085c79357a4122e7668172777574ef |
It is interesting to compare the Path of Destruction method with image inpainting techniques. For centuries, conservators have studied how to best restore art pieces by inpainting parts that are missing or destroyed due to e.g. deterioration or vandalism. In recent years, a burgeoning subfield of AI poses inpainting as... | d | 242d58cfd6b1d06e934567ff3f030133 |
In the first simulations, we consider the following setup {{formula:5ba58e2a-cc91-4c87-99a3-b05c60698a4d}} , where the cluster of AP serving each user {{formula:5e696ce3-6dd8-4343-8894-932d6849e3a3}} is formed by the {{formula:b29a5d71-f069-4b14-8b1c-a20783e1e021}} AP with the largest values of {{formula:69263e59-d83... | r | 0ef53af85233ce18bc99b8066a2704b8 |
Another promising line of improvement, though not widely explored, is the use of ensemble methods which combines predictions from multiple learners. Modern deep learning architectures rely on stochastic optimizers for training (e.g. SGD). They are highly sensitive to initialization, choices of hyper-parameters, and dat... | i | 4d9bd70ed40793ea8aa5ab094fd06104 |
CIFAR-100.
Similar to the comparison configuration of CIFAR-10, we show the experimental results of CIFAR-100 in Tab. REF .
Compared with the competitors, our lottery jackpots retain better top-1 accuracy under different pruning rates, while charging less pruning cost.
For instance, our method can achieve a top-1 accur... | m | bbf15d788f698aa45e9dce376600d148 |
Obtaining Community K-Shell (CKS):
Community K-Shell concept incorporates knowledge of information flow in a network. Obtaining CKSs comprises the following steps: identifying community structures using Louvain’s algorithm {{cite:3aac60f2c7369ad954fea656a0344820e15ec5db}}; isolating communities by removing connections ... | m | 84c1a73618e4d270c9d86ad2e42512e6 |
The modular Hamiltonian has been used to derive the holographic entanglement entropy in the AdS/CFT correspondence {{cite:38845888b481dacb65857fc26589963d10937540}}. Another importance of the modular Hamiltonian is in the derivation of the first law of entanglement {{cite:63d7b4200774c66b49008ce1e91889bda6df62db}}. Giv... | i | f45de7803b4a4f5f034ea1e28cbb2724 |
There are several limitations to our current study. First, our models could benefit from being trained on more data as shown in the training curve from {{cite:eac2c8a25125d255f4df0f619e621f7c144debd2}} (Figure 1.c). Second, our models should be validated on larger cohorts, encompassing different patient populations, tr... | d | 16b1231ba8a36b10f112a617f727bc3f |
We compare ZORB to BP with Adam {{cite:eff9738584d6b853feb8a985b46652af1c0379f4}} and Multi-layered Extreme Learning Machines (ML-ELM) {{cite:1f1b5767c99f4926e423038384a505b1cf2cd8a9}}.
For consistency in the optimization process across algorithms, we train feed-forward NNs to reduce the sum of squared error.
To compar... | r | a421f1e02f78110eb11c4ecb43ec1c09 |
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