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While the previous works enhance our understanding in competitive interactions, i.e., interactions in which gains can only come at the expense of others, MARL in cooperative settings remains largely under-explored and constitutes one of the current frontiers in AI research {{cite:73ef338b33171f76da1ca439b30e926628aff72... | r | a412da52b50b3610f0ec9d27690d2136 |
OTOCs demonstrate several interesting physical features in the integrable and nonintegrable systems. For example, magnetization OTOCs ({{formula:eeed0fd9-2d30-4248-b6a8-d22bef815c83}} Magnetization) point to dynamical phase transitions{{cite:a9e869d3ee72f72bc00a9fd5268e56931d65fce9}}. Disorder slows the growth of {{for... | i | b84ca6bad41fb1a00ea7b4b42492778e |
[itemindent=-10pt]
-
EIL {{cite:3642ef8b14d4c7ab41ddd03d3cd2ee0ed4fe10b7}}: It introduces a novel adversarial erasing technique jointly exploring highly response class-specific areas and less discriminative regions to obtain a complete object region.
-
SPA {{cite:35ce4ff2272827d7147b69fafec5b06b818c41b7}}: It explores ... | m | 6379812972aeef1a26c62c4962d21760 |
When estimating {{formula:353deef4-c211-4ce2-be3c-03e2c0b79295}} in Section , we employ a regular lasso regularizer to encourage element-wise sparsity, as commonly done in the covariate-adjusted Gaussian graphical model literature {{cite:2f0126893a9bc6e94791cf1afdb9efc40617b695}}, {{cite:f6fb4a766f5a85dc0a100d2b42a626... | d | a2a8a9128c1c3f3c7e883348cc4ce3ac |
We compare GRASSY to three state-of-the art frameworks for molecular graph generation: GraphAF {{cite:184f2c341612b4cf734c1a0dac47c2841fc4a5ca}}, MolGAN {{cite:2fc717eb2fec9647056decf41221c21f816be74d}} and GSAE {{cite:de0a2431adffc6fdc271a30a96c59148764cb316}}. For GraphAF, we trained the TorchDrug implementation usin... | r | badbde0abcbb6543f940a7e458fdf812 |
Although self-supervised learning can outperform supervised learning on the ImageNet size dataset (1.3 million images/1k classes), supervised learning is still a better pre-training method on JFT size dataset (300 million images/18k classes). The gap may be compensated by training with more unlabeled data for self-supe... | m | f355cf1c203ac2970865f977eaef2fde |
Our experiment realized Wu and Yang's gedanken experiment {{cite:89f72c983996dd130768be7cb9530e265f94ceb2}} to apply the generalized Aharonov-Bohm effect to the {{formula:04424b7b-e602-43d8-8957-c87d277a58a4}} isospin doublet of neutron and a proton, as an isospin gauge field detector. Such experiments remain impracti... | d | 1db6534fd7f7875440c5712aff8ddef8 |
a{{formula:49bf4f42-95e6-4e2f-be00-5418d5eeefd9}} Let {{formula:c992de74-95e4-4325-ac13-1c72721f0d0b}} be the ball which {{formula:20708d21-128c-4fa8-9a88-7552570754ac}} is supported, we put {{formula:922aedb8-4410-4253-b33f-b633b949f70e}} with {{formula:ec4ac8af-fbf3-4969-8813-e4c41ab5a96e}} . We decompose {{formul... | r | 506c85180a3b8786a39870d2554810be |
Backbone. We used ResNet {{cite:637d4c6d7dbcfb6eed407862df099c457b950603}} as our image encoder to obtain 1024D features from {{formula:dc149b85-7932-4f52-b955-5bd8a2d321cb}} images.
In addition, all relu layers in ResNet were replaced with leakly-relu layers.
| m | 747f18bc5e2e6c270d514188dc3db016 |
In CPT, the distributional character of quantum fields is taken into account. As a consequence, the {{formula:07f273d3-728b-4e5d-9706-f19272b3dc5c}} -operator is a functional of the switching function {{formula:bde89d3f-a35c-497f-9666-00af6f41f8a5}} {{cite:8c8f592cf71bf90e19e78649cdda59c345e44a75}} that multiply the c... | i | 77adedc47288f69fe9ef74d3a329037c |
The performance gains from using a pretrained language model instead of training a transformer from scratch are underlined by the performance difference of these two models in our experiments. The model trained from scratch showed AUROC lower by by 0.079 compared to the pretrained one. There are known challenges in tra... | d | dbb336b8379153455e107e5d7b754a8b |
The second type of architectures includes the schemes developed for the cat-basis encoding {{cite:28b1119366b0c1fd5357dedbaaca95fd938316bd}}, {{cite:ecb4b518a5280bbd1a3f13d3a3a0519f96d41143}}, the GKP encoding {{cite:a7fe2b220b82d0abaf6a791443b4c7a687fe9c40}}, {{cite:2771bd29f18ff3e217daca536d7dd605f4616567}}, and the ... | i | a0e06bb2460c3d47f79bd6b1455d936b |
Decoding and Vocoding:
Each content vector is summed with the style vector {{formula:bc1d3d01-0e78-4a02-81b2-f97aaa55a82c}} .
The resulting sequence is then passed into a Tacotron 2 decoder network {{formula:36c5ed12-c0e3-4bab-9aa9-aab1180efca6}} to produce an output mel-spectrogram {{cite:9ffd77bac647061580c96c098bfb... | m | 90ecfa8479ee7a0fcb6324e49c3f19ae |
Assuming that {{formula:0eb37fc6-18a5-43d8-8991-b4edc2b5460c}} and {{formula:39818320-e26e-4247-a7e4-6199f9d5333e}} are accessible, Golatkar et al. {{cite:86fa0f90084d596da093aeb7ff04dadfc0ebb756}} propose a robust scrubbing procedure modifying model {{formula:187173d3-47b5-43d4-b05f-ee6ce403a742}} , to brings it clo... | m | ba4ad20d5e2b6cca1d1a18a8702b4c8f |
Based on external datasets, previous work maximizes the error reduction by modeling interactions among various types of signals such as uncertainty estimation, input features, and the state of the sampling process {{cite:2a4b31d9472c927e69b66ad40c4d1eef1a839542}}, {{cite:90a7f8bea825b0070dc2e87bd4a756dc44ec60a8}}, {{ci... | m | 0b2dbe91ddeafc2c72e7591c90f78346 |
To complement our analysis of the poles and the channel couplings we present our predictions
for the compositeness of the dynamically generated states, see Eq. (REF ), for both
the {{formula:20719318-8ff6-4937-a70f-852802cbc8fd}} and {{formula:86186f08-1b92-46cc-8783-73a3e793b673}} limits. Our results are shown in Ta... | r | 7cd51356610dd553d1d8afc34a0d66d5 |
We compare with methods based on estimation of the transition matrix (Forward {{cite:77826cf3aeb8d9265c8dc8d246412cab2c54d801}}), design of loss functions
(GCE {{cite:bb9105ec0107ea9f59e2e877e7269a2fdaced1d2}} and SL {{cite:d101256f4b56d38dcc0babe9f459f8bcb41570f3}}), training two networks (Co-teaching {{cite:217bb4c9c... | r | b7b3232e9e5a0b2ce3273d697befce2b |
In this work, we analyze different sources of fairness risks, in terms of commonly-used group fairness metrics, when we train a model from imputed data.
Extension of our analysis to multiple imputation (e.g., MICE {{cite:1eaa104e956dd0f59f23fd6f22d02e9d7ca447f4}}) and to other fairness metrics (e.g., individual fairnes... | d | 95eeca2d91d5f145d3af83303609f7e5 |
On the gym domain, we notice that the performance of PessORL and CQL on datasets containing expert trajectories is not satisfying, often not as good as BC. We believe it is because of overly conservative value estimation.
In fact, it is widely believed that conservative methods suffer from underestimation {{cite:09840c... | d | baac800a8d0403ee339ff0ac88ddcb2b |
Data Processing and Training. We follow {{cite:925ba848835e62584c2d930d9dcf9b1c5079665e}} and extract 64-dimensional log mel-spectrograms with a window size of 25 ms, and perform normalization by mean and variance of each frequency bin for each utterance. In the unsupervised setting, we adopt SynergyNet {{cite:5830bf81... | r | 615eb3a815ef936097ac64d9bb5ea99a |
The possibility to move down along the spin temperature scale depends on the interactions in which nuclear spins are involved. These include, in addition to the Zeeman interaction with the external field, the dipole-dipole interaction between magnetic moments of nuclei, and their indirect coupling via electron states {... | i | 063a79dfe7dffd76f5a979a0d2e8373c |
ANCE-PRF also outperforms several strong dense retrieval baselines and produces the most accurate rankings on almost all datasets.
While Luan et al. {{cite:2e58a348a1691bbe3d0ac55fa84b89f5abee7119}} discuss the theoretical benefits of higher dimensional dense retrieval as in ME-BERT, our empirical results show that a w... | r | 6f9110943919b244870646ebfbff6e06 |
In this work, we considered the case of nonlinear models in the overparameterized case.
However, typical applications of MAML (and meta-learning in general) implement relatively small models due to the heavy computational load of running bi-level optimization, including both outer and inner loop.
Our theory also assume... | d | 91a69321fb9552f5e666bde1af453ccf |
The theory for the zebrafish regulatory function could be refined using the experimentally measured relative affinities of the binding sites at the her1 and her7 promoters {{cite:ecda026c730f2a990eb4d9737a97143b935e829e}}.
Apart from the effects reported here, the number of binding sites may have additional roles.
For ... | d | 806d027d77646e8dd5fcaa77a81baffe |
The most related work to our model is ViT {{cite:6825c010e88823b3c01f0ad8e7041d007e0cba90}}.
Here, we discuss the relationship and differences between them.
First, both PVT and ViT are pure Transformer models without convolutions. The primary difference between them is the pyramid structure.
Similar to the traditional ... | d | c6bdc66630e0387615a5b20f622028dc |
We review and implement seven different attenuation
models describing visco-acoustic wave propagation.
Each model encompasses a different
effect of wave dissipation and dispersion.
We carry out inversion with attenuation model uncertainty,
that is, we use a different attenuation model to generate the synthetic data (... | i | ba9fad1448c84091d9544f0bda1533d5 |
As an alternative to the proposed framework, label space dependencies could be exploited also through adversarial loss (AL) objective functions. Such approaches have been used successfully in natural image super-resolution (SR) {{cite:a4e96c66ac5ba0800a9c0ee2656a720e88faf8b4}} and segmentation {{cite:b0a17a82c13a6b4e93... | d | ba156cceee6280b3850cfff8e23906c1 |
Finally, by re-plotting the data as {{formula:77e11bc2-0c23-4093-9e4a-3b09d6b87836}} against strain in Figure REF (c), we can compare the experimental results to predictions from two-dimensional weak-coupling calculations, taken from Ref. {{cite:dc514fd264c31e89f2dabf7b348ef81d575b20b9}}, for even- ({{formula:930d1225... | r | 881f5c20dc6de69372d6ddf92b8b74c2 |
In this brief section we check some of the robust formulations proposed in this paper.
It is rather straightforward to extend the Nyström discretization based on global trigonometric interpolation and Kussmaul-Martensen singularity splittings for the four Helmholtz BIOs {{cite:47d8feaa3d57038559f60bb388f7d5e3fa34ad41}}... | r | d8d8c304066ca6d1e8f340aa65888130 |
[nosep, leftmargin=*]
BERT {{cite:ddb054a9366ded1f42096e36e7e3d65b42341be5}} is a text-only auto-encoding pre-trained language model using the large-scale mask language modeling. We fine-tune the pre-trained BERT-base model with the few-shot training samples on each datasets.
RoBERTa {{cite:adb68ff9d4cdbdda5fe101c987a... | m | 8671320abc02fd5272833df9fee3f5e1 |
Let {{formula:0f228efc-daa1-4957-ab1d-f25e2cf425e3}} denote a stochastic signal fluctuating in time governed by a particular dynamics. The persistence is then the probability {{formula:145d32f8-494d-4dd4-a544-1a637d9154ee}} that the quantity {{formula:bd90aff3-27f9-4c7d-ad3a-7af752178c9d}} does not change sign up to... | i | cefc2ce94631322fc95b10bd1a859d49 |
Despite the substantial progress, there are still numerous problems unsolved in this emerging direction {{cite:0475b83b4238b6d2c5c73365c199eae34bebd3e6}}. Among these problems, a critical one is about information evolution, a phenomenon referring to the dynamic variation of information content during its diffusion. Alt... | i | 6e57228243d62551155aa068bc816ba5 |
In Fig. REF , we compare the NMSE of different channel estimation techniques. Considering {{formula:f1e27642-0ed5-4e7d-958f-db9b1106f124}} , we take {{formula:57d78a4e-2c3b-4fde-8890-feed08c3286f}} as per Lemma REF . Observe the significant difference between the NMSE values for the sparse signal recovery-based techni... | m | 99caf5c67b55974b5ff22cb40c412b76 |
The modelling of reactive behaviors is a challenging problem that has attracted increasing attention recently. Many uncontrolled agents encountered by autonomous agents exhibit highly nondeterministic and multimodal behaviors. For instance, a human driver may choose totally different behaviors under the same situation ... | i | c9c86ac18ba3c11fc1be12871e13cb74 |
Another area of focus could be to extend the financial pre-training corpus beyond Reuters News to include other news sources, as well as additional sources of financial text such as SEC company filings and transcripts of earnings calls. Reuters News has specific style guidelineshttp://handbook.reuters.com (accessed 202... | d | add54507c6a864b26e39b7ca134be73d |
The new measures of coverage we develop here—tailored to partially
supervised data that may be easier to collect in many engineering and
measurement-centric scientific scenarios—help to bridge a gap between
typical conformal predictive inference methods, which require expensive
supervised data, and problems with partia... | d | 1af887b5b329a5c8c95b1ef99dacc862 |
Applying the Rice formula (see {{cite:8b14b945a3ec440bcfb8f869b97db33e4af619ee}}),
{{formula:a8a6af45-3a4a-434d-a976-92fc2b578c13}}
| r | 9b6c0fd523aeb42c41335ff56815ede9 |
The one-dimensional Goldstein-Taylor (GT) model {{cite:8ea42459bfa3286a210ab0fb5e96541766f0d739}}, {{cite:040cb31ba82d532b9fc496bea0e9fb406c86d0df}} with random inputs is given by
{{formula:1bacd80a-3478-4e06-91fa-3c36d9510a2d}}
| m | 1cf8d9ba80e56aa496de1af5eff37c75 |
To achieve this objective, inspired by the small-loss trick used in noisy label learning {{cite:f2051bf1f87a39938f25443d8613830302fb5545}}, {{cite:b1352fc2eeac9f7ad4558b39f66efc3d76fdd89d}}, {{cite:694d5bd5f613d44921e00017fd87b5146664c90d}}, we split the target data {{formula:0c8caa0a-4f1f-43dc-81c8-a0fa9cea51f0}} int... | i | 3f0f7a6905a7b67a8cc03b26be389ee2 |
We constrain ourselves to the case of linear processing on both the transmitter and the receiver. For the case when each user has only one receiving antenna, the optimal linear schemes for MMSE were derived in the papers {{cite:cb0cafc0f621a48df8162cef4f04e1b7cc08eaaf}}, {{cite:365d02accfe8c11e257a29a57d616bdb6acda58b}... | i | bfbb7a0b42d491f3527998005357591d |
Results.
Table REF presents the results. Our method achieves an average mAP ({{formula:b69e602e-7b62-4c77-9cb2-c45f14c4ddb7}} :{{formula:5337fc15-4e44-4217-aa4e-78e3c5d2f2f3}} :{{formula:4fcfe1a2-5f3b-4c51-8569-844c8efdc14b}} ) of 23.4% and 21.9% for verb and noun, respectively. Our results again largely outperform th... | r | 853dbbb2ba5124e97e041e1199b54fa4 |
The objective function in (REF ) is the sum of a smooth term, the squared norm, and a convex nonsmooth one {{formula:574a44d6-8fe3-4208-ad71-11f4c40a118c}} .
Problems such as (REF ) are called structured composite convex minimization problems, and can be often solved efficiently by proximal-gradient methods {{cite:2c30... | m | 5fd48dcaea99f25605af2727794a830a |
Our attention-based model showed significant improvements over the baseline models, but still has some room to improve.
For example, in Section REF , the BERT baseline model did not perform significantly better than other simpler baseline models that used sentence length or co-occurrence information.
We also observed t... | d | 0fde48f88fa911247baa3e065dfc79f2 |
Evaluation setup. Prior works {{cite:d561629b248ac4d6849aa5b9a45a546f5655fcf3}}, {{cite:72049d57f45c6a63a7b652277bc9229026e0845c}} show that Conv-TasNet, originally proposed for speech separation, can also be used for target sound extraction. Further, ReSepformer proposes an efficient transformer architecture for speec... | r | 7b0ff1215a07c6650ba06e229faf3168 |
The objective of this paper is a class-agnostic counting network – one that is able to flexibly count object instances in an
image by, for example, simply specifying an exemplar patch of interest as illustrated in Figure . To
achieve this, we build on a property of images that has been largely ignored explicitly in pre... | i | d041398ccdc3d27dc12af292de4a1450 |
Remark 5.2 (Error metrics)
Theorems REF and REF provide rates of convergence for the distance of the iterates {{formula:27b359b6-afe0-4ea2-9d54-712556842a93}} to the minimal norm solution, as well as the angle gap and the margin gap of the normalized iterates {{formula:efed4ac8-7c64-4b36-9e86-8807afce0ed0}} to the... | r | 4117b76e3d9653986ddc222824eba267 |
Magnetic fields also influence other interesting observables such as the entanglement entropy and the butterfly velocity that can be studied using holography. In {{cite:44c3b5086e858bf586d3000f96af27f4b0efa675}} these observables were proposed as tools to disentangle the effect of pressure anisotropy and magnetic field... | d | 40bdac59bcbaa523a4899b59d307b42d |
Further, our approach adopted the three methods from Aghabozorgi, Shirkhorshidi, and Wah {{cite:1ee870c779f1e035951857ae5972bac6c29acd21}}: the shape-based method (raw-data-based method), feature-based method, and model-based method. The shape-based method matches the shapes of the two-time series by a non-linear stret... | d | 8b020d2ab79a7fc5408c2c9fd507abe3 |
We solve Problem REF by using new compressed cover trees on both sets {{formula:d5c85f1f-8407-4431-b034-2527ac704946}} .
{{cite:a2029b60f4ee67a5462c9428f0b8ad97a7e3549b}} introduced a first version of a cover tree, which implicitly repeats every data point at infinitely many levels, see a comparison of two trees on th... | r | 2f2f20a29098fdade0e9a67c555c6c5e |
For over a century experimental efforts have probed Einstein's theory of General Relativity (GR) finding agreement with all its theoretical predictions {{cite:3cda5e983181e8974593e9255a8d7ef438321cae}}, {{cite:1f84aad14839e6e9c43af7795f77bbd533164574}}. Even though its original formulation was based only on Einstein's ... | i | 78c7a7dc9f8e1bcdf43c21caaecd9a6a |
Although this might has an analytic solution for a low dimensional cases, for a high denominational problem it is intractable analytically and we have to use numerical methods to evaluate the integral value. Here, we use the sequential Monte Carlo (SMC) algorithm to sample the posterior. The evidence is a crucial quant... | r | 6d44be5a02e74d515e3202f1a988d0d9 |
On the other hand, {{formula:7b488e93-d9ca-48e0-8a58-8d9942dfa866}} -ray emitted in the dissipation region might be absorbed due to the internal {{formula:74a0fb80-39e3-43e3-a3cb-dad95a57ebc9}} absorption. For {{formula:612d0914-43fc-4303-8f76-512d8c3f08fe}} satellite, its detection energy range is from 50 MeV to 1 T... | d | 3f4fe9467ad5a9a3f11d0e8b4e62a9fd |
Middlebury {{cite:c6cf8e7d4924e453017552f2da2f5a0b00caa948}}, {{cite:090ebad459d26ed734140cb53ca7b518d4c1d90a}}, {{cite:44d95703f94c7cc2f54a9afc9f5f8caf481656c5}}, {{cite:b2ed31f1ef2bc5801ed42d5ee64ae5802630482d}}, {{cite:960d7e357ff0d987cf6e747266e5cba19293ab63}} We use all 50 RGB-D images available from the Middlebur... | r | 41fc3e58cff7046402342b7d1ff61161 |
where {{formula:8f09bb49-44a0-4de7-a791-dc383e9e9244}} are tensors and {{formula:21fdf848-cbac-4bae-b15f-68a66faad1fc}} denotes the Einstein productwith order {{formula:dcb6d197-095f-48cc-89cb-3e77bf4685b1}} {{cite:eb8742b6556a8ee9ccfbeceb37b338fd184cb658}}. Basically, there are two main approaches to solve the unkn... | i | cf5a5d8c3e510ed8f547d062a8ca19df |
The time- and temperature-dependent expansion coefficients {{formula:268ee487-221e-4c07-9126-092ece456b73}} correspond to an {{formula:230c1f25-b866-405b-83de-922ea3784d05}} complex array which requires storage space and computational effort that grows exponentially with {{formula:40fd9060-bf5b-4bf5-9e7a-ac3eb1856859... | m | 37935079154f3582ce1b6bc2ba7d34d8 |
Caranti and Vaughan-Lee classified algebras of type 2 over a field of odd characteristic in {{cite:6b49a04fa9cc4e6ac23138a606ce94ebbf784cce}}.
They showed that all algebras of type 2 which are not graded subalgebras of algebras of type 1 in the way described above are soluble,
and belong to an explicitly described fami... | i | 87f0cc9d17b66f8a5f14bade3af83dff |
Model Training. The model is trained to predict {{formula:9989780a-2ea9-4dc5-8a6c-8284fa05842c}} from {{formula:1f4ee0b8-3e7f-4b63-9463-1e6a3bed3f02}} , for an unknown but fixed value of {{formula:0d425174-244a-4f9d-8596-6abb6151322e}} . We use sequence-to-sequence transformers {{cite:5225ab85e7f898e8d805563635db34d86... | m | 54fb102679222ea58e965688844ac621 |
Fourier phase gradient (PGS):
The most
common template-matching method used to date (and the default
method used in the psrchive software package) is the
so-called PGS method described in detail by
{{cite:69accb7daf42b860bea95fc03923c161fe43f533}}. Based on the Fourier shift theorem, it matches the
template to the obse... | m | 887595b165aa455ab115e8fdab86dfc4 |
for some {{formula:e1892304-ed4c-464d-84dc-15924babf235}} and {{formula:48be0b8a-9bee-43b7-80dd-c5f650feea4a}} .
It follows that {{formula:1f764db4-c20c-481f-bb58-5a52f044ab8c}} , where {{formula:10f242a8-d54e-4931-bd80-65196bbe1315}} .
We now recall a classical result for linear stationary iterative methods of first ... | m | 8b1a719688dfbaea05dc64e73bf4ad0f |
We show that ML classifiers (Logistic regression and LSTM), when used by themselves directly on time-series measurements are dumb to the temporal/ causal-structure in the data. This fact has also been discussed in existing literature {{cite:b2cea9c2c8e449442a7118d06b2dd9855945271b}}, {{cite:a59ba8e1ea29e6da08fca1f343cd... | d | 5968e09b4a060b3abd66cc47944ddc58 |
There also exist some hallucination issues. Retrieved knowledge can alleviate this problem {{cite:bcef1a866c155096b3f25bd86baf69ebe8c552eb}}. A pre-trained reader can also get advantages if the pre-trained LM itself also performs well for open-domain QA, as shown in T5 and FiD in open-domain question answering {{cite:7... | d | 04e3478e6f2e26612850b577025d3482 |
The idea of using particle filters to sample general distributions,
including those arising in Bayesian inversion, maybe be found in
{{cite:ed8cf583cf3aaeb24d758f25babc1d3acf7f391d}}; a recent application to a Bayesian
inverse problem, which demonstrated the potential of the
methodology in that context, is {{cite:233ec... | d | b29e446d27727755107114e05f54681a |
We have illustrated the concept of Domain-Aware Zero-Shot Learning (DAZSL) and Generalized Domain-Aware Zero-Shot Learning (GDAZSL) setting in the main text and Section. REF in this supplementary. Here we provide more details. We first present the statistics of benchmark DomainNet {{cite:f585524dbd8503755998519a642071... | r | 67667af9b4293af7e31dada87e8ba703 |
The main accepted galaxy formation paradigm predicts that galaxies grow hierarchically through mergers with other galaxies (e.g., {{cite:1d0c8248bd30772760dcf14c315ec54af7eefb81}}, {{cite:2a3b7480eac7311f7b0bf146929b7147f95d61c7}}), and thus the accretion of diffuse gas and dark matter occur especially into the halo. T... | i | d92577ae0402de69ffd5baef35053be4 |
Roughly speaking, the main problem with the continual learning setting is that areas in the parameter space that guarantee each task's performance can have arbitrary shape, see Fig. REF . Thus, our primary goal is to detect weight in the intersection of such regions.
Even simple linear models together with an intuitive... | r | 561b26603104cde335f7e666e6aa5fb5 |
To pursue more efficient VSR networks, we propose Residual Sparsity Connection Learning (RSCL), a structured pruning scheme. Specifically, network pruning has three stages, including pretraining, pruning, and finetuning. In the pretraining stage, we train a powerful VSR network. Since current VSR networks do not use Ba... | m | 60892bea8c464dbef6eb7c13c5e7ef74 |
Beyond the results presented in this paper, there are few directions that need further investigation. First, while the current UQDeepONet method is mostly based on the original DeepONet architecture of Lu et al. {{cite:95877758ab3fe912f63f6249267ddabd71e9ee1e}}, it would be interesting to extend it to include the modif... | d | 82aa37687299ece5ec634f53fa84f376 |
These results suggest that the pre-trained ALBERT generates a relatively stable trajectory, with higher dimensionality in a certain short-term range.
Moreover, the gradual changes in the benchmark scores around {{formula:fb5a180d-7270-4704-b54f-f4f9fbafc044}} indicate that NLP functionality is not implemented by the d... | d | ee8b8f448e474159a0936734237df4df |
(1)
Branching ratio for the {{formula:fd577d77-f570-4e17-85c9-44dcbae46983}} {{formula:d6deef93-498b-44b2-8e5a-0e52bdfc4e13}} {{formula:25274ef9-d067-483e-a01e-4be0032b2f74}}
decay can reach up to {{formula:6edc6c07-a745-4ff0-8363-462b939e8a56}} with the pQCD approach,
which might be promisingly measurable at the r... | r | dc1bd966486a788c6b2b74de788c49dc |
To avoid ambiguity, we start by presenting the problem of our interest, i.e. robust SSL, and common fine-tuning methods for evaluating the learned robust representation. Then, we briefly summarize how prior attempts {{cite:f1999b915c2bd69d16ada3bece7a3a8d9a827585}}, {{cite:8b02b7b27a34263598a36d36b91350bc60e356e1}}, {{... | m | 72b2aa880aebf83afa2e34059a2b9d4d |
The initial literature search has been conducted in 2020 and was updated in 2022. The retrieved body of literature roughly covers a time span from 1995 to 2022. The distribution is heavily skewed towards recent publications across all hermeneutic circles. This underlines the radical change *tr has undergone, rendering ... | m | fb87e219444ad0585348564cec31052a |
The proof can be carried out along the same lines as the proof of Theorem 3 in {{cite:a6fad59ead1a13ea4c2c6551dda40c434cd3cd6b}} by noting that their equation (A.2) reads in our case (this is a consequence of Lemma REF )
{{formula:a9c7cb50-837a-48ed-b8f7-1f361ae09a6d}}
| r | 6b56dedb69d4fde7349ec71d27d0268b |
We will also unfortunately not have time to explore Bayesian model
selection. This allows one to quantify the degree to which the the
data prefer one model over the other using a quantity called the Bayes
factor. These have not yet been widely used in particle physics but
should be kept in mind as providing important c... | m | 3039f08208c90b5c49f302a2ae908feb |
For practitioners, this model offers a great flexibility with a closed-form density that is relatively easy to work with.
Only a few statistical models proposed in extremes literature possess a density describing the bulk and both tails {{cite:baf7e00f5505c215dcf12ab1f332fa689ca07dd4}}, {{cite:5257beebd4ff3ebea5d38b2c2... | d | 0e33d1b22b11402daa9233703befb36f |
Notice that, in the in-in formulation, there are two types of interaction vertices with time-ordering and anti-time ordering and the corresponding propagators connecting them in the dS bulk.
This is related to consider both signs for analytic continuation instead of our prescription (), see {{cite:55b43ac6af335e6de7184... | d | bb27e06d6f5c0cc1a661bfd49436decd |
We build on the characterizations of {{formula:80aefd96-fcea-4260-9191-6a8cedbbdbcd}} and {{formula:f546c3e4-913c-47f9-9c2a-c4864a9cfd4a}} given by Hylandand Wadsworth {{cite:092377c34a2aa34db67b1b43511a38e413113c59}}, {{cite:bebebf858f09628fcdf13132caa8c0ff9ec1731e}}, {{cite:e78f8fbb10a3eadab2513cf6e3704c3ed38a0763}... | d | 913fb6a6f30a0c4f9c17ad27df6fbb2c |
Here, {{formula:41c54c24-4ede-437f-a7e5-d1888d819ac0}} is a matrix-wise indicator function that returns 1 for elements of {{formula:5280e882-632a-4ee0-b308-71b5be2d0d34}} that are less than 0 and {{formula:68e179de-5313-4e0a-8ef2-40b0117c900b}} returns 1 for elements of {{formula:e42846f4-7f62-412c-a7a4-57f5a10cfd21... | m | 2b1620da2f9c508753e324a6efb5b3a1 |
Driven by the promises of learning to learn, meta-learning has shown that automatically learning neural optimizers from data is possible, achieving results close to the state-of-the-art for the task of training neural networks {{cite:0950f36a210cbd90b50748ef61d7376a43e072c7}} or solving inverse problems {{cite:66f92a54... | i | b387282415ab0822fa1fab3f32cc8568 |
To quantify attribution changes before and after feedback, attributions overlapping with irrelevant features were computed using four methods (see tab:attroverlap): Saliency {{cite:8343d77b044287e24fb2bac4a440ecfd251b42be}}, *deeplift {{cite:86bc8ea08eecd18c80dfbc8cdfbd800f1a0169dd}}, *gradcam and Occlusion {{cite:5005... | r | d7b22a2ad3e342e05156684a418cca06 |
where {{formula:be0210ca-0c31-498e-8eba-2d46d36488bb}} is an operating loss, {{formula:50f79b17-b920-4213-9177-b7c546ae6c22}} is a candidate probability function with {{formula:b26b71dd-7ee3-4c46-ad90-dd11c65a3bb3}} being the candidate probability of the {{formula:f808943a-7052-456d-8da7-921f4faffa31}} -th pixel, {{... | m | f73985f20af28923a3508617fb4f29c6 |
Connectionism takes a different, brain-inspired, approach to Artificial Intelligence that stands in contrast to symbolic AI and its focus on the conscious mind {{cite:786823c8e647dbb1d4107644e0b07d621b4e034c}}, {{cite:3ad8fde58d9213d1b77aa90193dac14b4c53966e}}.
Rather than relying on hand-crafted symbols and rules, con... | m | 874601a5a88d6847ec4c87b44668bde9 |
Let {{formula:aa3ecc93-5460-4527-9b6e-dd4cca9bd1fe}} Then {{formula:5cca571f-4468-4121-b0c1-3f50afda0574}} since {{formula:42b251cd-607c-41e8-b2aa-daaecf032dd9}} is dense in {{formula:44028d6e-f8ee-4ab2-983d-6f0258d31c8a}} (from its atomic decomposition). Define the functional {{formula:d53b692d-756c-433e-a81b-8464... | d | 70fdfbb857dee05b92407425b276e61b |
The following theorem is a bit modified version of Theorem I, p.64. of Cassels {{cite:56f818d89e67f27eafbcad1f1b07c2190c4a7cbb}} and it plays a crucial role in this paper.
| r | bec5374537e203127850506b1d36ecc3 |
However, the rehearsal strategy relies on stored data, which is undesirable for several reasons. First, data storage is not always possible in practice due to safety or privacy concerns. Second, the approach is difficult to scale up to address problems involving many tasks. Finally, the rehearsal method is questionable... | m | f7a510ac5a0257b221a2d55df1cfa143 |
While assumptions like additivity and linearity could, in fact, be
successfully overcome in the algorithmic modelling culture, other classical
assumptions inherent in the parametric modelling culture did not likewise
magically disappear. It was earlier demonstrated
{{cite:9260c1083dd19818cacf6d9c852c83861bde1fba}}, {{c... | d | 67896fafe606dd727818cfffb682fb4a |
In first place, we reproduced the old results obtained with MSTW2008NLO PDFs {{cite:5499ad04134cb694f3b08aca8ac9f8d6d4959cc0}} and DSS2007 fragmentations {{cite:2430c3ac9cde38b96b00a8b9b3be76034fcbc353}}, but using the new Monte Carlo implementation within the LHAPDF framework. Then, we explored the effects introduced ... | r | 3c1b6f5ff949a5df6f7cac5cf3d7b8a4 |
One way to do so is through a process called weakly supervised learning (or weak supervision for short) {{cite:ea52a658bfed82c5dcd5b578ef4e40e6e3edd478}}. In this process, we ask domain experts to define labelling functions: rules that they think are indicative of a given class, such as “IF {{formula:1dd8429b-f5d0-488f... | i | 4223978e83caa3d79d2313172689ad0b |
The results show that the 8/24 {{formula:79bf5ab9-5aa6-425b-b9b2-71d1cf019515}} m ratio decreases in many regions with high SSFR, in agreement with previous findings from {{cite:1e23d4fe0c39525cab68b7d66404ae5e5da2234a}}, {{cite:b5e25922f5623f9864b7f4c84eb59f9b05866e0b}}, {{cite:576a7b2183cdac720c3fa97f9dbfa8ebcd08d562... | d | d3820cbf885588353fc93e3f06767a02 |
Early SSL approaches—e.g. Scudder's {{cite:fe33beb248c94a313a1e672adec86e8a67527576}} untaught pattern recognition machine—simply replaced unknown labels by predictions made by some estimate of the predictive model and used the obtained pseudo-labels to refine their initial estimate. Other more complex branches of SSL ... | i | edc1e9870ea9aebcdcff1a6a63189d7c |
[leftmargin=0.4cm,rightmargin=0cm]
In this work, we exploit local and glocal clues together since use of both local and global visual features play a critical role to disentangle the weaknesses of each feature with other's superiority. More precisely, local features tend to attain better performance under severe scale,... | i | 3130b59f34ea7f09f0b42df2fca5ace8 |
In Figure REF we show the quantum average power as a function of time, for a fixed external constant bias {{formula:f010ac2c-3cd6-447f-801b-ab2eec099123}} . Different curves are obtained considering different approximation schemes.
The choosen initial conditions and the finite external bias are responsible of a finite... | r | 4e261731d62f1a5f4f13610ca87431ce |
The fact that the dynamical behaviors portrayed by neurons are remarkably complex, demands the requirement of statistical tools to study and quantify their complexity. Measures like spatial average density, and entropy are effective tools to study the complexity in the dynamics of neurons. Banerjee and Petrovskii {{cit... | i | dce1382410e4a864d44aec04d3faf173 |
Thus {{formula:8ce4ea28-8500-4ccd-a99d-943ebc1c85ab}} in our sample is not significantly correlated with {{formula:7b7256dd-612c-4df3-8efb-02bd47a358fe}} , but it is with {{formula:5ecc2809-b27e-405b-8705-4f4a780b2508}} at {{formula:e8b47cfd-9192-4090-b849-0f82f60fce96}} 99% significance. This indicates that changes ... | d | d8950aa38c5d5ecbdc40f37b1290587d |
The original paper of Kalman, which is arguably the first systematic
presentation of a methodology to combine models with data,
is {{cite:6582d7a2e78331fdf29520b85fb82a4fe814f39a}}. The continuous time analogue of
that work may be found in {{cite:b3a78a4340b0f3169ad1667edcd311226af5914d}}.
The book {{cite:ba3bbfa539944... | d | c5753d037043b67a05244d45675d5e97 |
Subgraphs can naturally be more informative than paths
in capturing the structural information {{cite:9d294d6d03674cce2dae35d55c934e007a41301d}}.
Their effectiveness has been empirically
verified in, e.g.,
graph-based recommendation {{cite:e6113a3669360e634e8b69478caa06b39b978015}}
and node representation learning {{ci... | i | 15d6827794152c0c70eb92acdc74d878 |
Complex systems have undergone intense, interdisciplinary study in recent decades, with network science {{cite:cf11ba1e4e187e19061489b646d80856f46b2c76}}, {{cite:5a0334a79a90e575b8c80cea9a37501964830182}} having emerged as a viable framework for understanding complexity. While early studies in network science tended to... | i | cd37dee551cf32af419da091940c7d87 |
The formation and evolution of voids is well-understood in the
framework of gravitational instability {{cite:50dd4a844fd3e88f22f553edaedecc91862e7b90}}, {{cite:cdb94ac08a0ff3848fb73d1500372dc56759832f}}.
However, when one compares void properties of observations and simulations based on
{{formula:3986d5f7-78a5-4d7d-be9... | i | 47ac05c3f721d69f9f8b8ba2406a4c5a |
Sign-concordant feedback was also crucial to performance, consistent with the idea that “vanilla” feedback alignment does not scale to deep networks {{cite:39fe71b3c2201e2f81abaf3b0093b2d142551596}}, {{cite:00f636671b556f96f135e05ca2ea9039caff3864}}. An attractive possibility is that the segregation of biological neuro... | d | 93d7d056a5c8ebb66e1607e815982d3c |
where {{formula:cb762a32-ae3e-49b2-8569-a7836510acf3}} is the transverse momentum of hadrons, {{formula:5e26bacf-2ecd-4c7d-ae3f-e0b015da7972}} is the number of constituent quarks {{cite:2b07e272abb673b98f2b58364e669e316202ade7}}. The {{formula:3b3494b5-5a7f-425f-b2ac-6ed46a149c61}} and {{formula:d21bdc8d-3254-42e2-b... | r | 050b0752cbd86fd2856183ac6a3bec97 |
Let us start with the calculation of {{formula:bb738933-d0a1-410b-b591-1496811be82b}} -particle (where {{formula:56e5fd2a-0b34-4ba0-bd9b-65af10e91d53}} ) azimuthal
correlations {{cite:a94af29142c35139789c04343a492c954538ebb6}}, {{cite:a716f05413b1f18068979cdfce349255dfc0e139}}, {{cite:8dd0cac5adeadb3133a14ebec9d7ef29d0... | m | 3ff5abcd442ec4594ae7773c0974d9db |
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