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However, an alternative approach of cold inflation may be possible. If one introduces a coupling between
inflaton and radiation, the energy density of radiation can be maintained almost a constant during inflation and the (p)reheating is unnecessary This alternative scenario was known as “warm inflation”and deserves so... | i | ab06dbfa8fe5897413d0ef78bff64e68 |
The parton distribution amplitudes (DAs) that play essential roles in describing the various hard exclusive processes of quantum chromodynamics (QCD) bound states {{cite:f0532bfd1a83141a66af4a20af2ca9858df21b1f}}, {{cite:e4a95db199345a321ac6a15d7b7fcb0c03155cdb}}, {{cite:d580f144418bb1a5012ea6eb7823662d2b447888}} via t... | i | 7d350609f2649c64d37c32a45e2ef582 |
In recent years, there has been a resurgence in the study of asymptotic symmetries and scattering amplitudes at null infinity {{formula:5641f652-6752-4c7f-81ce-059fc284c97b}} , much of it aimed at formulating a notion of holography for asymptotically flat space-times (cf., {{cite:a3180ee23da54e9b959dd3278bc4c95cd765733... | i | 60f10040fa3e8cfdf4ca78fd94119d09 |
Generic search uses consecutive quantum searches until a solution to the search problem is found, such as the method given in {{cite:2ce5f83d976ad3d702219176083ee16b48b832eb}}. The difference of generic search is that it employs varying initial quantum states for the searches. Thus, we expect that the optimal number of... | m | 660100eb75f3f51d9c228e69e84358b0 |
We observe that each feature aggregation method brings its own trade-off between latency and representational capacity.
The representational capacity of a model is typically correlated with its number of parameters {{cite:e926979df4af631880401e88980721b8971796b1}}.
This is also reflected in tab:comparison, as a higher ... | m | fada253ad27d8eb858db76a73904631f |
In a many-body system, the von Neumann entropy {{cite:cf37e84f1689b464873652e323f3caa09033b4ed}} quantifies the changes of information from one local system compared with other correlated systems. Instead, the parameterized entropies {{cite:6538762c8b958b31c5e42486aaba2ff504acdee3}}, {{cite:6a762225862e611269040d9e2092... | d | b59e93527205d97c91c32f6ed08ac3fa |
Swarm Intelligence techniques have been shown to be effective and powerful in a wide variety of computer science areas.
Depending on the problem nature, continuous or discrete search spaces may be properly defined.
Accordingly, the different Swarm Intelligence approaches, such as PSO, may provide more efficient solutio... | d | b6d768a25a35979159ab7ad62ed0cb1a |
Non-monotonicity in generalization error gathered a lot of interest recently. Many studies pointed to absence of overfitting in overparameterized machine learning models, signaled by a peak and a subsequent descent in generalization error as the model complexity, or the number of parameters, increases, and the model tr... | d | b5d47167bbab70754dce775466ee332d |
Table REF shows the correlation coefficients obtained for the CPC model for the first ten epochs (CPC-2) for both languages. The relationship between the validation loss and the ABX across-speaker score shown in Fig. REF was also reflected in the correlation coefficients obtained. Both {{formula:d6ebebc2-5f88-46be-8c... | r | 88fc47dd0adc5e995bbfc96275bce306 |
Softmax: embedding extracted from an embedding network trained with softmax objective in (REF ),
Gradient reversal: embedding extracted from an embedding network trained with gradient reversal strategy as described in (REF ) where {{formula:00097a03-056b-44e4-ba99-65e983e73177}} was set to be 0 in the beginning and ... | m | 8b839488e976af70b53fc73aa8726768 |
Estimates of the effective temperature (T{{formula:9a4f8d5c-e5dc-4466-a1a4-bd9fbc5a5b78}} ) of Y dwarfs are difficult because theoretical fits to their spectral energy distribution are of modest quality, which indicates that the chemistry calculations and/or the sources of opacity may be incomplete, and values estimate... | i | 9a2abe7b9f095c823e14575e038f4edc |
A special example of our formula is the symmetric combination of stress tensor OPE block({{formula:667d0409-cd36-4524-9fef-75112820d346}} ,{{formula:9af1598c-bb72-49e9-9805-1117b4cb8eb8}} ), which corresponds to the modular Hamiltonian {{cite:33823296945c985542bd90d1180bc96348303e89}}. In our formalism, each stress ten... | d | 19c7d1abe931af876e4920d00d577597 |
The majority of the submitted algorithms are improved from state-of-the-art methods such as AutoScale {{cite:de817bab38898e6b5065dbc206d843ba564e4500}}, CSRNet {{cite:4afa80542bb3e7ce805d4e4097f532b5699c8042}} and SANet {{cite:654c69a05e9300c546c0ae5153339d89b5e412a2}}. FPNCC (REF ) is based on AutoScale {{cite:de817ba... | m | 4358aca7427dabf4fc2852f75d7d2031 |
Naive baseline* (Base_clf) {{cite:d588bc9c54c6b7ea8020d8349b571c43ab6bc7f8}}: Base classifier trained with D&S labels.
Simultaneous Expectation Maximization (S-EM) {{cite:727eb6fef8436380008790073ea92a4b79e13a55}}: An algorithm that jointly learns the classifier and annotators’ parameters using EM algorithm.
Dr. Net... | m | d1d215997313f919e3a1e6ce27ce6a40 |
Unlike the class-incremental setup {{cite:679b21faa087be41bfc1c23068b3543403522b12}}, the simple NT in ODICS enjoys an on-par performance to all considered regularization-based methods.
For example, while MAS outperforms NT on earlier domains, e.g, CS, the overall performance degrades to 40.3% compared to 40.5% mIOU fo... | r | 367dd653156236d22e36926cb1883b07 |
Grad-CAM {{cite:0260897baa90c101fd6335235ddf31a03e8f99af}} is gradient-based saliency method that computes the gradients of the target output with respect to the final convolutional layer of a network. The layer activations are weighted by the average gradient for each output channel and the results are summed over all... | m | 660121ba64f503aaea7f620c3db3fa69 |
Based on distribution-augmented contrastive learning, DisAug CLR algorithm {{cite:309b7f63a256c75acdf65c20ec2e32ef033a7629}} first learns self-supervised representations from one-class data, and then builds one-class classifiers on learned representations.
| m | f4d3ae080e64d13b4aa8c68828915637 |
We also see that regularization approaches such as EWC already fail at the first increment. In contrast to the success that has been reported in prior literature {{cite:3095069e388d2be93217ee6915f7da8c46638155}}, {{cite:b321befac270d824c833f4eda882530d9dcce941}}, this is due to the use of a single classification head. ... | r | bd505317b9e705e094b73faf5f4cfb7f |
In recent years, aided by the growing wealth of BH observations, studies have worked toward constraining BH natal kicks using a variety of methods and data sets. A number of studies have focused on the population of massive runaway stars with large space velocities, considering the possibility that they may have origin... | i | fd5854649ebf719c30298fd2ecc9cc84 |
To predict the ratings given by users, many of the existing methods are based on Collaborative Filtering (CF), which models users and items with their historical interaction records (e.g., ratings, clicks, etc.) {{cite:4d9b342b97d7dcf0b078bb6a787c08f800d9a09f}}, {{cite:71c88b189b9018febbbc2ecda3b98a79b04354fe}}, {{cite... | i | 6339e0aa72cfffaeb040474b3e69a775 |
On the other hand, classical optics, which tackles vast majority of
physical-optics experiments and is based on Maxwell's equations, has never
ceased its own evolving steps, physicists have endeavored to develop various
optical transforms in light propagation through lens systems and various
continuous media. The two r... | i | 567ff3bf7ad77f666799b04919c631d8 |
This paper contributes its own classification of quantum gate sets by giving a complete classification of the so-called stabilizer gates, where we allow the swapping of qubits and the use of ancillary workspace. To provide some context, stabilizer gatesThese gates are often called Clifford operations in the literature ... | i | c1a507afe88273ea508dcbecf68299dc |
We built the OntoGCN (ontology-directed Graph Convolutional Network) neural model where known similarities/relationships between the features (genes) direct processing in the network and help the model to avoid learning spurious correlations {{cite:e5c27b6d6c043bc5ef608eb35ac3f570b8814dfe}}. OntoGCN enforces convolutio... | m | 9d6c7d3d102f9ed330403340de8c1d61 |
Smooth trajectories obtained by minimizing jerk or snap have been widely used to control differentially flat dynamical systems such as quadrotors {{cite:8b0d4b08ef41347c79132bbcea95acc2cb04449e}}, {{cite:a3ed7a9a823605fb23ebd4d02e7e1855e1d1e7c1}}, {{cite:40b9ed4337f80aa9448e63a5550fbef3a8790828}}. These trajectories ar... | i | 8f018f6553d219f8921150d980239853 |
with respect to a set of parameters, {{formula:dc3fde27-aa6e-48d4-8df2-5343b9793374}} , denoted compactly in the above equation as {{formula:c4d657f9-7b7e-4f19-b263-6efbb80c2883}} , is an upper bound to the true ground state energy of that system {{cite:b4bffaf7bed191f08cde4dd8753978f53f9dd938}}. {{formula:b1bf6adf-36e... | m | 63b6f4465d5973590a9f9788708fdf0a |
which are {{formula:1572078e-d682-416e-894b-c5ea830ff0ef}} -submodules of {{formula:da29f241-a2c1-413e-a58f-e83e0e67a9be}} and {{formula:e7bc2497-1902-43a7-8f35-2a16298a525d}} respectively. So {{formula:577a5bf0-5586-4d47-b752-2914a82ff9ef}} and {{formula:dc93c5c0-662a-4fad-abdb-f0f2d121d266}} Recall that to solve ... | r | 6236be85464902e94f665c798f8ba9dd |
MagFace. The method presented by Meng et al. {{cite:7b1dee02bfcf79f63723420500b014426b4495bd}}, called MagFace, generates both an embedding and a quality score for a given sample by using an extended version of the ArcFace {{cite:c2b15203c8733eec2e7d32bf6a5269662af520ae}} loss. The proposed loss is able to discriminate... | m | 2b6c34b5537a448f0ab442fff40483ee |
To analyze in an organized way, we sorted the results in each dataset based on the METEOR metric, which is the most used (see Figure REF ), at least in the reviewed literature.
After the results were sorted, we took the top five in each dataset. Table REF shows the best five results reported in the seven most-used dat... | r | c80be30b91c88e0d18317b1f55dd420a |
Figure REF shows the traditional single-label Fast Region-Based Convolutional Neural Network (Fast R-CNN) model (in blue) that has been extended by adding multiple labels into the model, where every label corresponds to a classifier for individual phonological parameter (handshape, orientation, location). The model us... | m | ca315a63527054bbaa40d45e75830d0e |
Another possibility is that the UV theory
keeps the large (but now regulated) amplitude of particle creation,
and a large outgoing energy flux
(which can be identified with the “firewall” {{cite:1c542748690b34e4afd92a422b7ea4c6e5a5a1d5}}, {{cite:3ee22e0cc626ddc11a1148aa993e67ddc3815b10}})
appears around the horizon.
| d | 1cfdc90b832631346d8c3fab26e688ad |
Motivation and high level description of algorithm:
We begin by describing the key insights that led to our algorithm, and provide a high-level description. First key observation is that standard ERM trained models might already learn features which are good for domain generalization {{cite:9555c1147856008c02d79442fdfb... | m | db60140e0cef5b7c56c4cbd82ec1903c |
Specific hyperparameters of BTC are summarized in tab:parameters.
The hyperparameters with the best validation performance were obtained empirically after applying in 5-fold cross validation.
Adam optimizer{{cite:4e85bfef069015606632ea7e5ac3cdd9cface006}} was used with initial learning rate of {{formula:c68a0ed2-96f5-4... | r | ace72a83b3ba12f64065954e98560ab0 |
We consider feed-forward neural networks with ReLU activations to examine how the network's final representation space connected to the gradient structure of the network. Our motivation is twofold: recently discovered knowledge about ReLU networks {{cite:8035f9ee66e505c9a7921a9d7e24395b6107a620}}, {{cite:11dd29223778e5... | i | 7f8dfacfb5672094f0e422507c3afd1a |
Research questions concerning pattern identification in environmental mixtures usually involve unsupervised statistical techniques whose solutions are obtained independently of any outcomes. Researchers apply common methods, such as principal component analysis (PCA) and factor analysis, to describe the variability in ... | i | b3cd6104fbf458ffd5bc28eb026b65ef |
In comparison, AE based pretraining does not perform explicit density estimation but instead aims to reconstruct the original data from corrupted input.
A notable example is BERT {{cite:6e99e6f7da33052ce1aac6e5b79e9221f80da5db}}, which has been the state-of-the-art pretraining approach.
Given the input token sequence, ... | i | 889d16d6640aa33455cea5755961381f |
Let us compare MLA with ULA (i.e., MLA in the Euclidean case with {{formula:6cd38990-582a-423c-b58c-1b547f701b12}} ).
Recall for ULA, mean-square analysis yields a biased convergence guarantee where the bias scales as {{formula:24287283-37ea-4cb4-aa43-2c1df4f03074}} {{cite:9ea9b6a3d4c595bfe83eeb26b389dbaa7e1a4f60}} un... | d | c6fc605034b05067199dbe0dc2ede978 |
where {{formula:f1fdd9bf-d754-4f64-87c7-506b33c8978c}} is computed by solving the linear system below to a linear tolerance of the user's choice; in this work FGMRES algorithm {{cite:7906af429cf22c341f22324d73b7fae28f18c558}}, {{cite:f1e649deedd60206c8244d87bba97c20766e97d6}} is used to solve said linear system, excep... | r | ee5d7050beee71d6c4894d23f110b402 |
The BW model invoked to fit spectrum data in Ref. {{cite:1bc77c4c6f2fe33feee7b688d05cbd92b67a54be}} is nominally adopted from Ref. {{cite:46b8f0246631c541035a986334b62647bec0d0f7}} that introduced a BW model to describe pion spectra from 200 GeV fixed-target S-S collisions at the SPS. The relevant formula is Eq. (7) (s... | m | 4c6ee9aa452229f0e00031b415e9a068 |
which corresponds to class II Heun polynomial {{cite:10674d66d78c8b53f1a035687b40ba41794bfcaf}}.
| m | 82d9ffb161bda6b75f5501ced498a875 |
We report the performance of different algorithms in Table REF and Table REF on precision, recall, F1 score, approval and fraud rate on ECD and IEEE datasets respectively. The proposed DQNR method performs better/at par with all other models on the F1 score except XGBoost on both datasets. XGBoost outperforms all oth... | d | ffebdb86f81765dbd66806dd27b00088 |
In our numerical calculations, we adopt the following parameter choices.
The default value of the charm quark mass is given as {{formula:2959d2f4-0dca-4d93-a083-50047aedf125}} ,
and the fine-structure constant is approximated as {{formula:664f216a-f5ad-4c6c-9789-92394951baff}} .
The renormalization scale ({{formula:3b4... | r | 0588e2a0e6300f2a7bfd4c15def1b995 |
Different from those in {{cite:9e70c9ffc9f6618140ed4e05b955b9238601a847}}, {{cite:b3df05b74febb3dcec8fc1e9a20aca5db6e416dc}}, {{cite:038e838775a3c9b180cd9ad917e7eb6927435cb8}}, {{cite:20ad01a2eb13036effdf18ed0342800453e62989}}, {{cite:645b771809f004fc7aef4d9589cce7a4a0d42211}}, {{cite:206b3141cee2985ed3a4ec18b97cb0ecb9... | r | e0a4dbec3d13d050f33864cb436b95b7 |
with the interparticle force (REF ), where {{formula:b750b9ed-5e9f-49c3-a7fc-5aacccad9ce3}} is the shear rate, {{formula:9ceb07bf-4559-4cc8-8b0c-a3f37c19f802}} is the unit vector parallel to the {{formula:6791965f-5a38-43f6-93bd-3896c9477370}} -direction, and {{formula:76aedaf3-a594-460a-ba1a-7081f2cdc8f4}} is the p... | m | f6874d754bd5b2d09555604f45a2c29b |
The second set of diagrams in Fig. REF (bottom panel) has been computed by both the RBC/UKQCD {{cite:e8f019e75657e80df24f3e575accb011a1cd8c0a}} and the BMW {{cite:2d469cc488a0c823d73fbbb211b1b6fe8626cb0d}} collaborations. They correspond to corrections to the quark-disconnected contribution, in the same electro-quench... | r | fbdd89a48718b837f06c6dd278e841ea |
In the present work, we have reconsidered the possibility of the detection C{{formula:ec407cf6-d7d3-491b-961e-c27969485d27}} B using a birefringence of electromagnetic waves in the relic {{formula:89e5e981-1f59-4286-b4d8-b1af57cd9db8}} gas. In an intergalactic region at present, in addition to a sea of relic neutrinos... | d | 73c5009747a2e298d92b19091195b7b1 |
Such an example is not just anecdotal. Indeed, it is accepted that networks trained on high-level conceptual tasks have their initial layers related to low-level features and their deep layers related to high-level concepts {{cite:71aadb9eb00abcdf80e2cc78e6c35bc306929054}}, {{cite:5a928fd369944f490fa1f8ecc471622678330e... | d | 3b5d2b22fb480f07b75677b6d30117af |
In contrast,
the area/entropy spectrum of black holes is known to be related to their quasinormal modes (QNMs) {{cite:fed408be011d59f82c62b7304a61090999216016}},
specifically, the imaginary component of QNMs in the large damping limit, i.e. asymptotic QNMs (AQNMs) {{cite:ae9234c886149398b5a328eb51ca13ab70bb4a4e}}.
To p... | i | ff56b707ff3c3d455a14ee07559d8b2f |
We evaluate a current state-of-the-art cross-lingual specialization transfer method with minimal requirements, put forth recently by Ponti:2019emnlp.We have also evaluated other specialization transfer methods, e.g., {{cite:73107e4d0e54d06ea7a98a1a118ba647f67fe0db}}, {{cite:d8a1c64cf113afa1f9641263e6ede9b3470223c2}}, b... | r | 75dbf3303efe8b9077234275e5cb9bbb |
LR {{cite:3af59a0b5d7293974c346d8c51e66ceec71346df}}: It's a widely used baseline and applies linear transformation to model the relationship of all the features.
Wide&Deep {{cite:f202206813a66cfde30f4ba0299c3a2cd397007c}}: It jointly trains a linear model and a deep MLP model to the CTR prediction.
Deep&Cross {{ci... | m | 63e2bc13954360552dedf4337513e540 |
Some cases require more encoder and decoder blocks. As the number of encoder and decoder blocks increases, the feature map size is reduced and the essential features are lost. Introducing the “skip connections" between encoder and decoder blocks to pass finer features to the decoder blocks. This modified FC layers arch... | m | 96d53af6f087c4b851531f34d753cfe9 |
Previously many studies focused on measuring and characterizing quantum resources in the framework of intrinsic and standard decoherence models in different quantum systems {{cite:1b25a829fd973e24c574d90a453843a80f49ee65}}, {{cite:def3fb4462ce225a5368e78a578132182e2c3841}}, {{cite:6de593fa753c85922da4fc4f3b46691442093d... | i | f6ea550ce15b09c40b72c0eb7c648f0a |
Solid-state materials, in either bulk or thin-film forms, inevitably contain disorder of various kinds {{cite:adce84c7114f5c18d535ed23c76fe607e2acba33}}. At finite temperatures, the otherwise perfect crystal lattice is constantly distorted by the thermal motions of the atoms. Besides this normal “thermal disorder”, whi... | i | 764f5421ab9f3be8497ab47d1478b583 |
We should also comment on the observable effect of the electron EDM in experiments.
The EDM of the electron is usually measured through the paramagnetic atomic or molecular systems, since the relativistic effect enhances its effect {{cite:4d99a9bf41dbf57684100794a315ba799a370164}}, {{cite:236f99496aacabc5c1e60264ef7970... | r | 5d55acc0cd06c64aae02272f77ca558e |
First, Definition REF and Problem REF rigorously deal with a potential ambiguity of {{formula:6fdd7c11-5146-4b36-992e-477436889242}} -nearest neighbors at equal distances, which wasn't discussed in the past work.
The main new data structure of a compressed cover tree in Definition REF substantially simplifies the na... | d | 9987a9da18397756bce0975b6cf40ba0 |
By almost-cocompact group, we mean a group which acts cocompactly on the {{formula:2e4edc01-fc2e-44f8-8843-550b0b281561}} -thick subsets of {{formula:672ad54b-cc4d-41b7-9d3d-b06a43bfab54}} . The same notion was introduce in {{cite:b63ad55d3b424cfd683197c57b0ae9e76931eb46}}, §8.4 with the notation InjRad{{formula:9f1169... | i | 965f6993203b64e6e9294d3fe48c4b43 |
The results for the SYNTHIA-to-Cityscapes benchmark are reported in Table REF . Following the protocol evaluation protocol of previous studies {{cite:444ab85e9a7a80cbf58cf6a6e4d8ad8e4bb91c77}}, {{cite:e3f627787bf839e01d2d545c1b888db395a6e070}}, {{cite:e43c17450d7b09cdd8ffc1e5a49499b7bb24c6a1}} we report the mIoU of our... | r | 045d30b261841a510d51ed323c7e502d |
(i)
The functional {{formula:d4033163-d4a6-4607-b43c-59668a7908d0}} is lower semi-continuous if {{formula:800a0005-e603-487d-9117-85d13a3ff6b6}} , {{formula:1dfd4ce8-e52d-4f57-9597-49f0e515d837}} , {{formula:c1baa365-895b-474e-aad1-cece781ae755}} is lower semi-continuous, subadditive and {{formula:97e2c79e-4da1-42fc-... | r | 64348e3a13afd34cb7f8954a0525b089 |
The simulation analysis shows that working with daily reported infections leads to better Effective Sample Sizes using a smaller number of particles, as the data spikes are reduced. According to Cori et al. {{cite:a6f4e48159148db6c9653e0f92f53e86361b48e8}}, the estimates of the instantaneous reproduction number are exp... | d | 674d554d60922cb5ecf8c7552889c793 |
Theorem 1 ({{cite:7ab874a4ce99a7a958a1fd3e82f83ed5f663ae80}}, Theorem 4.3.2)
Let {{formula:54451a80-2b5e-473d-8d0e-d77bdb27cb5a}} be a real symmetric ({{formula:08208cf6-660c-45d7-9705-e31657bc5134}} ) or complex Hermitian ({{formula:7f54faae-22d6-4582-b1e2-d7799400a99c}} ) {{formula:7bd6ee49-fe42-4f9b-b5e6-806f1eda... | r | 707c40d9e58769ac9024e956ab131052 |
Motivation. The underlying principle behind our proposed method is that semantically similar images of different classes are often embedded close in Euclidean space and are consequently misclassified by k-means. However, soft labels may provide more information about semantically similar classes, and thus by using sel... | m | b64be8f69c6d474701befbdbe120516c |
While the line of argument above is –in hindsight– very natural, the conclusion is broadly useful. For instance, {{cite:8a544c939efc00f80f39c4861488d22605b332b6}} study a class of
of message passing algorithms inspired to replica symmetry breaking and survey propagation {{cite:95b581ff8c2e957ceff39c4e52bba6db2298bdbe}}... | d | 8a1c706fe3f143f0e99795bd08ffab1b |
Additionally, in Fig. REF (c), we put the real image {{formula:8cdf9bbe-4387-4c4a-bad7-5b4b6fdbe2ab}} with the corresponding domain label {{formula:ed0f887f-d684-4222-ba5c-56d1ee3908f0}} into the style and content encoders to get the content code {{formula:177a7a88-15f9-4222-bee8-f6ccc7d81614}} and style code {{form... | m | 59aa6ad069633aa525b6d35663e74cf5 |
Recent works on offline RL, however, mostly assume that the environment is modeled as a Markov decision process (MDP), and standard offline RL algorithms focus on reward-maximization only {{cite:2ef0538c39e54c1586c7668bdacb9bea3655f474}}, {{cite:8a8f33df3f50bb00fbe733dd18372f5ace21a052}}, {{cite:84cea38bcc265f473d3bd0a... | i | f4c1117ee6f7d0f472b2bf76f2f165bf |
While {{cite:2fa2d2d1e3f7de84503d01ef5543f27429777812}}, {{cite:006a1158e0bfeb189a900ba493b41f4107cc5967}} use standard architectures (e.g. YOLO {{cite:6bd0c8eb87c4171358bd4065512bc29ebcf05dd6}} or ResNets {{cite:c7ab50ce7e6e9be4c921d5ba15087414499c7cb6}}), they require a large amount of engineering complexity in mani... | m | 2f27e707320a83b3e0bebd90446be072 |
-
{{formula:6cd9f975-cbf0-478d-9933-b32befc0cd3f}} to compute {{formula:9456e187-4553-4815-9b1f-00a72da87085}} and {{formula:63a3830e-cdff-40c8-a02c-0a152c46e89f}} to compute {{formula:cc7ff564-104e-4503-8995-70d4b24eef6b}} and {{formula:0667a17d-f913-4933-bff4-26376aa8a70e}} to compute {{formula:e5e68fdd-fa7c-472... | r | cf1f4111c62c4d80df35ba854a8979e4 |
The resulting composite dataset enables training unified semantic segmentation models that come to a step closer to delivering on Papert's vision. MSeg training yields models that exhibit much better generalization to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a proxy for ... | i | 7bed31e741cf0fd0373ada036c5fee56 |
Existing domain adaptation methods usually assume that the source and target domains share an identical class space {{cite:4ccbf1de6e5f312210a262108a69d0416f5085e2}}, {{cite:b70a45373ad78136d98745910b29b946e35f225d}}, {{cite:1c45c901faa8a17f90cbf1d4e7aaf7f030311a03}}. The assumption is easily violated in practical appl... | i | f602eb82d99e5facdf2c02232a00c5b7 |
The current implementation of CBL is order-dependent, insomuch as estimated subgraphs for the same dataset may vary if columns are reordered. This can be addressed using methods previously devised for constraint-based causal discovery {{cite:883813a68588ce1d9fa62addfa0024b78588cb47}}.
| d | b423a3b8dd419d4571793272efb5f6ed |
Denoting by {{formula:da219d44-255b-45aa-83f0-c35738f58404}} the set of finite measures on {{formula:d47a5a49-c14c-427e-a58f-a63b86b8c436}} , the process {{formula:7a887d3f-f7d5-49f2-bcd7-dcff2c14a5fd}} is a càdlàg process of {{formula:6802ec04-fda7-4fc5-b83d-1402e4f3187b}} which is the historical particle system, f... | i | b28ef79060461e7730ba0f8e515d9bfd |
In the second step above we essentially apply the curvature estimate in Theorem REF which holds for any {{formula:5e71d318-d3a8-4509-9078-5137b858df9a}} and any sequence of {{formula:a0da409d-5c54-4174-88d9-a9afe7e4e450}} {{formula:c326042a-ce6d-45ff-ace1-ac77f3ab82ec}} -perimeter minimizers with good controls upon ... | i | f479ffecbf605506799670e9b932e04f |
2-c) Continual Learning with Elastic Weight Consolidation (EWC): However, naively optimizing a (Fourier based) Contrastive Loss {{formula:d47ea191-9606-4e53-8872-899033668312}} as above, although bridges the domain gap, leads to a catastrophic forgetting of the information learned during the burn-in stage. Thus, we a... | m | 3aad283619053857fc3dcdd79e0a7e1c |
The following lemma can be found in {{cite:053f0db500e6f6d4984554b24949aad5952cc262}}.
| r | 7b14a41df1207eba7d4271c3b1c110a1 |
Over the past decade dark matter (DM) direct detection experiments have improved their sensitivity by an order of magnitude every two years and this trend is expected to continue for the near future {{cite:523c338bcd81ca2e1bd89873588ab29e9c4229b1}}, {{cite:e15bf4a61f8e729521f16d77372c0841128c91d7}}, {{cite:d0941d25d5fe... | i | 0444d52cab42c5d2fe444794183e3bbf |
In the future, we can try other methods of approximating the partition function such as generalized belief propagation {{cite:5cc007a9f6e9841264390c5ca95b8715821996c4}},
which takes advantage of higher order Kikuchi approximations of free
energy. Unfortunately the structure of our graphical model causes
higher order in... | d | 373f3d768c143be83b20cf963f1e8a3f |
Nyström Approximation: These methods approximate the {{formula:b2e4b223-496c-461b-960e-021f34839ff4}} covariance matrix {{formula:97452b3c-41dc-42b8-84ca-7092e1cdf888}} by an {{formula:85ec481f-cfda-42dc-b856-b43b142fb2a3}} matrix {{formula:5c725792-63bc-45fe-82a9-c7bc8342361f}} , where {{formula:0cfdf5ee-897e-4da4-... | m | 4957d4b6adee1b54789c84b81ed97a8d |
One of the potential issues inherent in our approach is when each video is first uploaded and becomes available on the system without any prior playback usage data. The “cold-start" issue is typical of many usage-based machine learning algorithms such as recommender systems ({{cite:5a5ee02e5708537137a25f60cf093c2d4b15b... | d | b047840c4b8e6061f0dd8540aa5b9c5b |
Model studies have shown that higher order moments ({{formula:14d7c5a9-b199-4b3b-a32a-4e6ae5170496}} {{formula:4ce80750-f818-40f6-9287-a3ef4fea2c77}} {{formula:21b3da28-4805-4e78-9fe9-f97b55b4bfd4}} and {{formula:fd28853b-59aa-4272-ba57-13dda2b2f7ed}} {{formula:b2fc0655-3c15-4cee-89fe-5fb184b7e556}} {{formula:c386... | r | cfad8e36c16558fdaca99547a052a3bb |
Reinforcement learning (RL) with IIR-SNN. Due to their inherent recurrence, SNNs with multiple timesteps might be more useful in sequential decision-making (such as RL tasks) than static image classification. However, application of SNNs in RL may be limited if the latency is too high, since the agent has to make decis... | r | d142616b38e6c354cf58c53b043ffacb |
SK16 performed a grid search for their parameters, where here we use a Monte Carlo minimisation procedure using the emcee python package {{cite:5853e9d4213b24884437f4b3d18c730a5710ecb4}}. We compare our population parameters to those of SK16 and find that we replicate their results to within {{formula:070e3903-c3c6-400... | m | 739ec676d47ccdc5123bbcb497be8b98 |
The vector, {{formula:cc089d63-6fc8-419e-9b81-c8ab41348a2e}} , is the ideal objective vector obtained by minimizing each of the objective functions individually subject to the constraints. If {{formula:48dc4cb3-7325-4845-be79-15507eee7128}} , the sum of weighted deviations is minimized and the problem is similar to the... | m | 1992fa5fb06aff696a1d9fd542c5cfb6 |
However, the decreased feature maps due to pooling undergo spatial knowledge elimination injecting roughness, poor border knowledge, checkerboard artifacts, over-, and under-segmentation in the segmented substructures {{cite:6a6537ed4e3e8715827fe21d59d0e06196fb0498}}, {{cite:2585acc21885f4035f562bddf9135c7828c3d646}}, ... | m | 3e1b83f1d8ae2840f40a382a949f51de |
[leftmargin=*]
Group (A).
Two-step feature extraction and classification methods include
(i) TF-IDF+SVM and (ii) LDA+SVM{{cite:250407de547edc31c386b0a3b8f8adc8c6f7bd3a}} which use support vector machine to classify documents represented by TF-IDF feature and LDA feature respectively; and
(iii) PTEhttps://github.com/mnq... | m | e9c4282719ba10a90e78286546998406 |
{{cite:b862731e209e936dd08b1cf157cc5866ee0e4c85}} 2018
{{table:9f175dfd-c46c-4feb-9f37-accfe76d4acf}} | m | 6251e2a03136cd3b097cfb3224de7793 |
Feature scaling and data normalization is a common practice in machine learning and has been shown to be effective in areas as widely disparate as deep learning {{cite:0d4c719d92f3e0eff567ba0b5d35a6d665f5e730}}, {{cite:70552f3df72d692e04944a523dc3839de615bd06}}, nearest neighbour classifiers {{cite:fe486c12519ac328fc... | i | 48a6cfb648964e489e27d137155f08b4 |
An important line of DA methods relate the source and target data by assuming the existence of a common subspace. {{cite:1e953c48b7831aa1e3b644a95082d709e7a1fa77}} first came up with a closely-related idea of projecting the source and target data onto a reproducing kernel Hilbert space to preserve common properties and... | m | 439a6482e8a408ed5443baf78ba0583a |
Finally, our run-time analysis shows that our new approaches have the potential to be very efficient.
Most of our approaches are more efficient than Word2Vec, which is characterized as a very efficient language modeling approach {{cite:d1a3d6e4045658267a9b0a5278ffb43c85f38908}}.
The key feature of our Cobweb variants a... | d | ebfad8b4e5ea342dd692d0010223b2f4 |
For the DFTB calculations, charges were converged with a tolerance of {{formula:df269280-dbf4-47ea-8086-ec2c63ea1a5f}} a.u. and forces with {{formula:7e5e75fa-7d2e-4e23-b556-82da1702f56a}} a.u. Bulk calculations were performed with an 8{{formula:db0a6521-8d8d-4a0a-b40d-71b9b9a17919}} 8{{formula:e751f3c6-13dd-4026-a24... | m | 08ea7c2be612c8be4a0c6ba49642e99d |
The results for this experiment are illustrated in Figure REF . In this figure, the graphs on the left and right show the performance of all methods for fusing multiple estimates when using projective Cascading ICP and Hybrid ICP as the underlying ICP algorithms respectively. As these two graphs illustrate, the choice ... | r | b99494654846de1f768786dff3cf2816 |
We adopt the following criteria to select baseline methods. First, the model should be able to parse inner facial components as well as hair. Second, it is open-sourced and we are able to re-produce the reported performance by re-training the model from scratch. Third, the number of hyper-parameters has to be relativel... | m | c00fa1e1d040617a69929496e6152c60 |
From a theoretical point of view, pure shock models (e.g. {{cite:c5fb6bca1c19b4e4cccf987703815c39e2fe4da3}}, {{cite:7adb12d3da6de5298bcd72223916b6078958dd99}}, {{cite:edaaf40a1a084824dcf33dc561f6b8c40aedb0be}}, {{cite:4e543ac5323d15b24031a4504b2fe422920ad6d5}})
and composite
models (shock+AGN, e.g. {{cite:1e619c7c01774... | d | ea6f568b701c775d9976dcc6f73c8da3 |
In addition, a clear difference of the band dispersions {{formula:0ab7bf3c-88e3-4f28-8cb5-9f2ac2500122}} ({{formula:049fd06f-e597-4e0c-8234-a995b50b7149}} ) between the valence and conduction bands is observed. The top valence bands are more flat than the bottom conduction bands (figure REF ), which is caused by the la... | r | 19726c3b21994ee3cac65b71d8538500 |
Alexnet made sparks in the community with only 2 GPUs totalling 6GB of VRAM {{cite:9f1dffeb067f41e9304f76529fe8589821668b2a}}. Techniques today spend tens of thousands of dollars on renting compute, or sometimes millions on renting or building super computers (eg GPT-3, alphastar). The amount of compute thrown into sea... | d | 041f99be72fa3304c27f484325db2f42 |
We have extensively described the typical nonlinear evolution of the MRI in fully kinetic shearing-box simulations, explaining each phase of the evolution and the differences between the small- and large-box cases, as well as between the 2D and 3D cases. We have explained how small and large simulations differ in prod... | d | 00e7968a0d3feb5ecd114657e167a29e |
Under astrophysical conditions, coherent emissions are typically generated in fully ionized plasma (a marked exception is the molecular line masers). The plasma is a medium with a long-range interaction, which, in principle, enables the coherent motions of large ensembles of particles. However, the same long-range inte... | i | 4be76aaadef3eb2b6588447b3eed9912 |
The projections of the correlation functions for the three different trigger particles are shown for two intervals,
{{formula:6c8894ec-b50d-4ced-9a96-f4fd1a175192}} GeV/{{formula:4055bbe2-3acd-4bbe-8814-7914ae20c4aa}} and {{formula:95d6c50f-1521-4499-9f4c-65012818f9ad}} GeV/{{formula:4caf94d6-6604-4041-adbb-ef78cf11... | r | 264c3545ce69f39ebaa284ec2302f88b |
Advances in computational infrastructure and breakthroughs in artificial neural network architectures (ANNs) are emerging. These lead to the use of machine learning to tackle this partitioning task. In order to process the amount of information in a single image efficiently, we need to use convolutional neural networks... | i | 1e593b174d40d9c1212a2c0bf7c1737b |
On the other hand, a wireless communication network is highly complex with many components and mechanisms, rendering the task of mathematically modeling the whole system analytically intractable. Most of the traditional scheduling algorithms are designed based on single layer of the OSI protocol, which can not consider... | i | 62ebfbb3b0d32d4fdfd096e871102bc8 |
Fabrication and characterizations of M-FE-S memristors. A vdW vertical architecture illustrated in Fig. 1a is adopted, to construct an M-FE-S junction in the lower part and a MOS-FET in the upper part. In this configuration, ferroelectic-semiconducting interface as well as a gate tunable semiconducting channel can be c... | r | 4af6c931ad2d3b105e08f7b175e6ed10 |
Again, the imputer for imputing {{formula:b3e3449d-e5ae-4a59-941c-e1baf6cdde22}} can be any suitable method such as SoftImpute {{cite:eb2de691ba4b79fd8c90cdfb84882249263c0234}}, MissForest {{cite:0085011dbefeb396d92c89454958c31c71692630}}, etc.
| m | a00ae711ccef5996cbe5ea49bcbccefe |
Deep Ensembles {{cite:2368bf3caa1b2e4b3b5f35565fe793d1e6337652}} and Packed-Ensembles are ensembles of DNNs that can be used to quantify the uncertainty of the DNNs prediction. Similarly to Bayesian Neural Network, one can take the softmax outputs of posterior predictive distribution, which define the {{formula:3f8749c... | d | 1d32f3e7258e8bbd21e89559176da6aa |
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