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Most of the surveyed SSL models contain a stack of Transformer layers following the convolutional encoder. Since previous studies have shown that the last layer is not always the most useful for a given task, we adopt the weighted sum approach following SUPERB {{cite:b286eaeecf0e18f8d846745ac7000d2ef5535da8}}. This all... | m | ec9c867db75faca3bb30d358dffc758c |
All graphs considered in this paper are finite, undirected, and have no loops or multiple edges. Let {{formula:aa34b887-966a-473d-b3a8-e6e95975cef2}} and {{formula:33f71035-5f67-4bfe-9ea3-871cd1c3b512}} denote the sets of vertices and edges of {{formula:3efe14cd-d315-477e-8ab5-b388ebe40eb5}} , respectively. If {{form... | i | 63fd16c5e3b3112fc874f0b905a0c2ee |
A generative model is a set of probability distributions that models the distribution of observed and latent variables.
Generative models are used in many machine learning applications. One is often interested in performing inference of the latent variable given an observation, i.e. obtaining the posterior distribution... | i | 0d7984c1a0480e0b429f64ee01442592 |
When we glimpse different manifestations of quantum chaos like hydrodynamics and ETH connecting to the emergence of RMT, it suggests to us that a larger synthesis may be possible. Certainly there are many connections between chaos, random matrix statistics, and eigenstate thermalization, e.g. as reviewed in {{cite:8f4c... | d | d8c59ba3e096f207e40b0c9345419572 |
We address these issues by using a two-stage training procedure, similar to {{cite:bddcc304887ccba9b439e749d74280b240095d05}}, {{cite:da79d303dbafc5065f8c91ec4b4498a5a3ca5ac4}}:
| m | 1bb2e173c17fb4da468bf7623e33e8c4 |
Table REF and Table REF additionally compares the driving score, road completion and infraction score of the presented approach (InterFuser) to prior state-of-the-art on the CARLA Town05 benchmark {{cite:e5a0d0f1fac96b48033a9bcc849ad7fb92a7676c}} and CARLA 42 routes {{cite:2166f672dbb17261b5f11524f4ba119ea07ec10d}}.
... | r | 5744f35e2901c021782edf63620da5ea |
Comparing our method to others for the SER, we can see that
we are slightly outperformed by VGGish {{cite:8126b98f165b8ffed6aec5972826c0cd545ec14c}}, {{cite:20f39418d3c6e2b2482c8d3d7d9b1c6c25d22d97}}, according to results taken from {{cite:cb95ccfbde6c3a231a13d49986f0d5cf8741e969}}, which has been trained with million ... | r | ae2e74e5cbd78f80328f95fcc8d1202a |
Theorem {{cite:c85e857b4272ccf5fe3bfb544c727b71f9190126}}: Each positive definite kernel defines a reproducing kernel Hilbert space (RKHS).
| r | deef278278b88f089e81e6a382451fff |
In general, we observe very similar posterior distributions generated by the two different methods. One can additionally compare the reconstructed observables as shown in Fig. REF exemplarily for the energy spectrum. In comparison with Fig. REF , one can see that both methods yield good agreement between the modeled s... | m | 68b266b4bdcf5db587a3722acfd94250 |
It is well known that the Blaschke product {{formula:9ba77fbe-d1b2-4380-a236-cc36284f5b42}} given by
(REF ) converges absolutely for {{formula:d85ed874-1347-4f90-9ac2-a2a009da54a9}} and satisfies {{formula:8c81d52c-e818-49ed-a95e-61063bcdbea1}} with {{formula:3b2eef0d-cfd4-4fd8-af03-4a46bd1a8665}} , since {{formula:... | r | 97187d736826674bb5b00a2cfaa01bec |
which solves the time-independent Fokker-Planck equation {{cite:4ae8a63c43e9089311df70459be57877885579af}}. Note that {{formula:7bb3ceb0-bfd7-41f7-8b7c-69be168d0a0b}} and, by extension, {{formula:c13ca8fb-daf9-4fbc-b49b-51648e87195b}} are symmetric under the
change {{formula:86c7d963-ac88-4c1a-9d7b-eac9c9ecc57b}} an... | i | a16c0f399eb6c2f76f18a45d8c67102a |
Looking Backward. Production systems were one of the first AI research attempts to model cognitive behaviour and form the basis of many existing models of cognition. However, in traditional symbolic AI, both the key entities and the rules that operated on the entities were given. For AI agents such as robots trying to ... | d | 982955799970697f61daf0a1edbf7191 |
We would like to give some comments to close this work. First, we
notice that the discrepancy between topological phases characterized
by {{formula:0dd78b0b-2c72-4009-8617-13b4938ceb65}} becomes vanished if {{formula:9adcd281-d8a7-4ec7-b877-ee8aa4ed4271}} . And
in the black brane background (REF ), the CS brane is exc... | d | 1ee9892235ef43c961e6f9313a8a1a9b |
Approach overview.
We adopt multi-task unsupervised tracklet learning
with each task dedicated for an individual camera view
as {{cite:a56d4d62dd4cf00337bafbd832bbb0bc943b0ce7}}, {{cite:0ca9b1efb241f410f698787dd5c435097aacb150}}.
In particular, we first automatically
annotate each tracklet with a unique class label
per... | m | e9d33d81e9cc5e0ef64668e79422c3f4 |
We also study how the entropy bias is affected by adding a threshold bias term or ReLU-activated hidden layers. One of our main results, Theorem 5.5, proves that adding layers to a feed-forward neural network with ReLU activations makes the bias towards low entropy stronger. We also show empirically that the bias towar... | d | 695d41c94291c47466da0b0fd2320d97 |
Our simulations show that massive clusters form rapidly in converging regions on timescales of Myrs, in agreement with previous work {{cite:52e93ede490f7c4e9f34e1aec8a54bc60b3efb92}}, {{cite:ae58d659f58da580508c04648c20736e2266c2ac}}. However whilst these previous simulations only considered simplistic cases of two con... | d | 7f0fee4369c83093f0af79b44290c7bb |
Comparing the three cases: Table REF shows the testing accuracy of the classifiers in the three cases under strong privacy guarantee on STL10 dataset. We have two observations from the results. First, we observe that Case III achieves significantly higher testing accuracy than the other two cases. For instance, the t... | r | 0ca8ee5f23ed693aeab860437f368b15 |
Generation of the DVS spikes responses along with the filtering was implemented entirely in Matlab, while the SCNN and its various layers were coded in Python using the Nengo-DL library {{cite:9df7a60fdd474690dd95db8e9f45e69b0eb41a55}}. The generated .mat files of the dataset were loaded into the Python network using t... | m | 09feff37dc7165f5107fa2c8aa446b1e |
Assume that {{formula:2e3092ed-6cff-4677-8723-ecf9deb1ffb6}} is always Hurwtiz and {{formula:721a5007-6f3e-4864-9afe-dd1a5e71bd31}} for some {{formula:9bee70ba-15d3-4448-926d-862c0435316f}} and all {{formula:72cd1a05-24c3-45bf-ab1d-44183e794187}} . In this case we can pick {{formula:080a2132-21e4-480e-b676-fb793b25... | d | 91f241ea9cd75c060de93bb0e988de1f |
On the other hand, as for the tasks related to the named enity recognition as well as the keyword extraction, we followed the same procedure that was done in our previous work {{cite:327ece3635736cfd274794441d50f59d52801785}}, taking into account that no new layers were added to the model, a linear layer was used to ma... | r | 6319a08136e37cd2404e0e34c8c70a77 |
Finally, interpolation-based re-ranking, which combines the benefits of sparse and dense scores, significantly outperforms the BERT-CLS re-ranker and dense retrievers. Recall that dense re-rankers operate solely based on the dense scores and discard the sparse BM25 scores of the query-document pairs. The superiority of... | m | 44fee945d18cfa124d802e9522d0a77c |
Music Information Retrieval (MIR) is a growing domain of audio processing that aims to extract information (labels, symbolic or temporal features) from audio signals {{cite:cd82cae8a46f6611cc8ec4771907bde8db081deb}}, {{cite:1a3d39807ad390a8003d252ddabac24cbc685b47}}. This field embeds both musical and scientific challe... | i | 17585ec07a3626b918e1e853c33c1e5c |
Experiments with relativistic heavy-ion collisions at facilities like the Relativistic Heavy Ion Collider and the Large Hadron Collider provide the opportunity to study deconfined QCD matter which is dynamically evolving in an out-of-equilibrium state.
Over the last years it has become emergent that the bulk dynamics o... | i | e43737667e674995a51807d5eac0d491 |
Next, we consider how previously proposed bias mitigation methods {{cite:0870676de9d9c59c5d5c3a780fa7cea04468a80b}}, {{cite:391361a3ec7a4543ceb7f3930fd535fa293d74c6}}, {{cite:f632a5dd25a8279c733ca1d96ae81b89514133c1}}, {{cite:6c47e97f607c191b4285c47bda66666db6ece9b8}} perform when evaluated using multi-attribute bias a... | m | 0af1732e5a2624a3115c7051394c8d3a |
In what follows, we shall also be computing the half-chain
entanglement entropy of the Floquet eigenstates. The procedure for
this is as follows. First, corresponding to any eigenstate
{{formula:b9fbec13-e8d6-4a9a-9722-d043d93a178e}} , we construct a density matrix {{formula:14075f54-a88c-477f-9e46-8f33c3d2dc9c}} whic... | m | 3f9d2527715c04403fc31f46fcb34f14 |
One of the most fundamental tests of the Copernican principle comes from observations of our motion with respect to the cosmic microwave background (CMB) rest frame, which induces a kinematic dipole that has already been observed in the CMB {{cite:bb7047d87f2da309314b5c61cb61399002cf63cb}}, {{cite:b5f9c0cea911a7b266c54... | i | a8e772496ad8b6d4253267d54c7990e4 |
Global contrastive
Global contrastive methods treat every image as its own class, while artificially creating novel instances of said class through random data augmentations.
In this work, we evaluate contrastive methods using Momentum Contrast (MoCo) {{cite:e45a78943275672b459c6ab2a8d8d932eb2e738d}}, and specifically... | m | 5dbb4f795699e9ba95342f7105cf1018 |
Two key processes allow the NS/BH to accrete mass at a high rate and launch the jets during this common envelope evolution (CEE), the formation of an accretion disk and neutrino cooling by the accreted mass. The density gradients in the envelope and in the core leads to a non-axisymmetrical accretion flow where the NS/... | i | 43e198dc614dbd433dad2b37dfcec1a5 |
paragraph4.2ex plus.2ex minus.2ex-1emEmbedding Algorithms. We use both fastText {{cite:67bc7f0a1363a87170d01874cecc412829d4cb23}} and Skip-gram word2vec {{cite:b443d6e885a1ccf779642cf913d51a18a03c03f2}} embeddings, as two of the most common embedding algorithms. Word representations in fastText are composed of both a w... | m | 5b1ffda177bbe40ed26ea0c2169e2be7 |
In this section, we describe our proposed method for sequential scene flow estimation and sequential point cloud forecasting.
Our model solves the defined tasks by exploiting several properties of point cloud sequences {{cite:1eabb218f706966be9ac71f3434826803aaa8504}}, {{cite:c9d82c918e7aea44deb704847ac5ff4c10756dc0}}:... | m | 9534fbc7c25bf4ca75963c35b65842d4 |
Due to the use of an optical cavity with the near-perfect temporal and
spatial matching, the memory efficiency of 67{{formula:a26abd2e-1ead-47b7-9fe7-901d7c145d14}} 1% and the excess noise
close to QNL have been directly measured. For set of input coherent states
within the mean photon number range from {{formula:39dab... | d | e79eca5a9e3bd4070946ad20c822993b |
Considering the target samples do not have ground truth label, the self-training methods {{cite:15c24148416d61638663e406365a5830ceb67ecc}} utilize the inaccurate pseudo label to calculate the cross-entropy loss. Therefore, optimizing {{formula:4f1ab6e3-0080-4ac7-b2e6-ce060bdd92f3}} can potentially be more helpful for ... | m | ad03a9e20f0a7b73a8d0a587d0ef879a |
The interplay between the predictive distribution and the restriction property of the DP is the cornerstone of the “indirect" proof-method of Theorem REF . This method imposes two strong constraints in the choice of the prior distribution: i) the predictive distribution induced by the prior must depend on the sampling ... | m | ece406ed65c881b54d99daac77c24d08 |
In the polar coordinate plane (see the left panel of Fig. REF ), the coordinate origin with zero eccentricity is no longer a stationary point. This is different from the prograde case (it is known from {{cite:8affdf86431621e709851efc5125a7f2aefbe86a}} and {{cite:16ca3ce1aa22f8498694998e7fb2dd5cfbc18f50}} that in the pr... | r | ed3d3c874b061e9e62212cace7aa3e6d |
Evaluation is done on datasets covering a variety of objects and settings (i.e., Named Datasets). CelebA {{cite:f10593d487c39ebee5da6ca41394ddc0feda8d1b}} is a large-scale dataset of faces. LSUN {{cite:562a1b50f866d8f7ddfe777060d8a756b27363c9}} contains ten different scene categories, from which we use the images of br... | r | c0f1892691c00f1ff0152a4d2b473f5b |
Although the recursive least squares successfully estimates the elements of the
noise covariance matrices, it does not guarantee SPD estimates of the
covariance matrix. The convergence of the estimates to the true covariance
matrices is guaranteed provided the matrix {{formula:f8cd2dd1-02c1-413b-922f-2a49a93ff844}} is... | m | 8e4747226d526ff4537d3c81a5b84d46 |
Thought weak limits are known for the optimal value in the regularized optimal transport problem, less is known about the distributional limits of the optimizers themselves. In this paper we provide the limits of the empirical solutions of the primal problem (plans/couplings), the dual problem (potentials) and the cele... | i | 3534906ca49d7160c18c36bdc9e3665d |
It has been a long-standing problem in space-time physics to resolve the mysterious relationship between information and gravity. According to this fundamental question, a recent efficient approach is to examine complementarity between quantum entanglement and geodesic structures in the context of holographic principle... | i | d8d11d192065524fa9c5a62614a8b5a8 |
Face recognition (FR) has been well investigated for decades.
Most of the progress
is credited to large-scale training datasets {{cite:0b05fbca8d18058a2539aa8ff1052bcaa52c8c1e}}, {{cite:43de7bd21483eaace5d20c907d778ee75ebe069b}}, resource-intensive networks with millions of parameters {{cite:858da22842ad9969a7288cfd170... | i | cdbadbd92d3b7b94a902b5e9db78115a |
In this work, we have presented a comprehensive exploration of the magnetorotational instability (MRI) in collisionless pair plasmas via fully kinetic Particle-in-Cell (PIC) simulations. With a shearing-box setup implemented in our relativistic PIC code Zeltron ({{cite:d6dc2f0a4139a1edef0afce03bfd3b4f1e5f7815}}), we ha... | d | 762cf812ad7539ad0a50524198c49383 |
The obtained distribution of waiting times (with logarithmic bin) is shown
in Figure REF (left column), while in the right column
is reported the same distribution divided for the binsize;
the last distribution is proportional to {{formula:e3cd1575-d7f9-4ed0-9546-3091cf5ac08f}} .
Fitting functions are superimposed to ... | r | 28c8f9ce2952d8863c4d092772c61d85 |
As described in Sec. , we find a linearly increasing complexity (or decreasing close to the recurrence time). However, the way the linear increase arises from the bulk perspective is different in our case than in previously proposed holographic complexity measures. In the latter case, the holographic dual to computatio... | d | 4f9dbed92ef3ec54914e5bf2eadc3d64 |
A black hole mass this large in Leo I is not expected from extrapolation of any of the standard black-hole to host-galaxy correlations. Of course, these small systems do not necessarily need to follow the trends seen in normal galaxies, but the black hole mass reported here does stand out. {{cite:aa68442155297b200aff5b... | d | 39609f7ae17b06c7d945e728dcaff36e |
Intuitively, the initial term {{formula:81af2ffc-15bf-4c95-ac15-447ac4b6b76a}} in regret upper bound in Theorem REF describes the regret caused by the “burn-in” period of exploring the space of contexts and it does not contribute to the asymptotic regret growth. Note that we consider running Thompson sampling from th... | d | 775c84bd85b2ce4f1742dc58990e7239 |
This work was supported in part by National Key R&D Program of China under contract No. 2017YFB1002202. (Corresponding author: Zhenzhong Chen, E-mail:zzchen@ieee.org)In the era of information overload, people have been inundated by large amounts of online content. Consequently, recommender systems play a pivotal role i... | i | bd457b2a36670e11572e69f4b4b336a5 |
Computational learning systems driven by the success of deep learning have obtained great success in several computational data mining and learning system as computer vision, natural language processing, clustering, and many more {{cite:e4e8c6dfeaeab086c545d2f21a0ebfec37e9360d}}. However, although deep models have demo... | i | bfe8e05dd05c962170cd796cc58c1d9d |
where {{formula:6ec19495-8a39-4d56-8cbe-eedaee88886e}} . According to the experimental data for 28 GHz channels in {{cite:2a7557207aa1281fd0e0a47c416406edd91dcd7b}}, the parameters in (REF ) are set to be {{formula:a53cfdef-ae16-4e5c-9168-df2213d25330}} for a line-of-sight (LOS) path of {{formula:8a5087a4-b691-4552-8... | r | a5304fd9e19d7a957984fc7a27120b7e |
Another option to reduce spin precession due to the prevalent electromagnetic fields would be to place a foil (e.g. made of Carbon) that is able to shield part of the fields. This setup would, however, be more in line with RPA {{cite:e2adbb4ebf0c109a555adfa00f6485a36d0d276f}}.
| d | 95992065b45c4f9fe7bb8454b1326147 |
Actually, one may go into broader generality and consider similar problems for any monotone property of words (sets of words closed under any alphabet permutation and taking factors). One natural example from outside the pattern avoidance setting, are words avoiding abelian squares (words of the form {{formula:458d6d9a... | d | 45c63bd352ed47600198aa9950c4adb8 |
Skyrmion number {{formula:793eccad-0fcd-40b6-a89c-c3fad7f7983c}} and injective scalar value {{formula:8a3fb698-0773-42f6-b9fa-4dc70a59761c}} . In order
to support the discussion of skyrmionic textures, the topological
skyrmion number {{cite:113461f3977d1ef9b928e8ca154677c88c4b238b}}
{{formula:f594ea99-bbed-4f27-94f8-6... | m | 66eba197904358011dc4201f7416289c |
As the binary classification method, we choose logistic
regression (LR), and use the implementation of it available from
scikit-learn.https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
We consider LR a good choice, given that it is a probabilistic
classifier that already pro... | m | 32091054f8264418a2453fa2ae619d86 |
The above power inequality was first conjectured by Halmos and a delicate
proof was given by Berger (see {{cite:7977d9f98f0beaf03a278bbdcdbcb03922fdf5ef}}, {{cite:be4716bea5f864687d73be9c6a2f3b149634be59}}). After that,
generalizations for polynomial or analytic functions on the unit disc have been done.
In 1966, Pearc... | i | 9cbe6951708028f932bbb37ef50b9a02 |
We further validate our proposed co-occurrence based mixing technique on the salient object detection (SOD) task and present a comparison with existing mixing methods {{cite:6360c52c2e70a627413f73639ab73873ef1fcf1c}}, {{cite:35d204dfe0b81ac68bf1ea6c9373fa2d8ef807a7}} in Table REF . Similar to the task of semantic segme... | r | fb417fb6a07b3fae4c237caff22c7c94 |
Scale-free graphs can be easily generated using a well-known technique called
preferential attachment {{cite:13bd0ef03de63b67546412163aefc6e173df72d9}}.
In a simple serial model known as Barabasi-Albert (BA) model {{cite:3b8fa15978ec81913ffe77d0837dabc44cd1e08c}},
a scale-free graph is constructed, starting with a smal... | m | 9ed9018099f5a821fb9f6a46ccc83701 |
The paper is organised as follows.
Our notation for the 2HDM-II is set up in
Section .
In Section
theoretical constraints like perturbativity, vacuum
stability, and unitarity are studied, while Section introduces
electroweak precision observables in the form of the
oblique parameters.
Constraints stemming from the SM... | i | 93314d4a711b9b32847c17b149c5af88 |
Numerical methods to acquire the LE spectrum by evaluating Jacobians from the functional form of the governing equations or by reconstruction of state space from time histories do exist for smooth systems {{cite:cd9629dd99c34ad1a4e203982b3c58b315b0edec}}, {{cite:7f53c0977f763bb8ce5cc2a64ff1230836a7152b}}. However, this... | i | 97514a4628de10c65c748c483fc02991 |
BTL-Uniform. We generate synthetic data using the Bradley-Terry-Luce (BTL) model. Under this model, each arm {{formula:b18387d5-3a96-4c35-96f6-67843d76a960}} is associated
with a weight {{formula:fe05b5ec-bf57-4de3-81eb-5a61d0fde770}} (sampled uniformly in the interval {{formula:bb46df3f-9002-4de3-af58-fb1c20c251f4}}... | r | 28ff8d626be6d795693d592da6e617f2 |
{{formula:7388c85b-3df6-467b-b48d-72824a8b1371}} Zeilberger's creative telescoping {{cite:145e4b3f67cf7c37dd82942fe1c5c7a2820e22e6}}: Given an algorithm {{formula:f6323b08-7303-4de2-9bb1-1ff36aa994eb}} that computes a bivariate sequence, find an algorithm {{formula:0791248b-a36a-432e-819d-f9e08dd30b50}} that is not ... | i | 75d5e0c2c4fc7c8c678677df21d265f1 |
where {{formula:e56ecdd5-3443-4189-b7b4-a6c34405e5f3}} is the electromagnetic coupling constant at {{formula:5f556bb2-bfe1-49c3-9051-a8c53516f497}} . The input values for {{formula:b9f788c9-b770-4c10-b87d-797705309586}} and {{formula:9bbca45c-d5e1-4b29-93d9-f5c91ab3944c}} are taken from the potential-model calculati... | r | cf9d3d7b03cd3da265b80bc36db51665 |
We additionally report results in terms of V-Measure (VM,
{{cite:8988acda1f730ed3f72bd405cd1d7d8aab334234}}) which is an information-theoretic measure. VM
is analogous to F-measure, in that it is defined as the weighted
harmonic mean of two values, homogeneity (VH, the precision
analogue) and completeness (VC, the reca... | m | 35357c29139e5ab523bf80c82f6bcd80 |
The mechanism described here could be valid, however, even if general covariance is broken by quantum effects.
For instance, it could arise dynamically in a cosmological scenario.
In a “long” high cosmological constant phase in the early universe, natural in the slow-roll inflation scenario {{cite:b2f2d19ee395885034823... | d | 489ec80414c94721163d0f10c006cac3 |
The audio segments are processed by extracting 32 Mel-frequency cepstral coefficients (MFCC) features. The input was augmented with time shift perturbations in the range of T = {{formula:b36a89b1-e608-4976-9452-a3f8cc7ca3ec}} ms and white noise of magnitude {{formula:9ec6f17b-b106-43a9-a25f-ad0eb5725be7}} dB with a p... | m | e79378a90404bb588b789d7c93fba1a9 |
See § for the proof.
Note the inverse condition number {{formula:1319c2c8-c80f-499d-a30c-4cd3a9c6bffe}} yields a tight threshold on the sensitivity of the population for the stability of .
Our result can be viewed as the multi-agent extension to {{cite:1f552f770c67429cf522505f8d28b13796997aa4}}.
Notice that while {{fo... | r | 1fc7abf0d44dd62068227c01a180dde5 |
With the upper bound of the Bernstein entropy given in (REF ), the bracketing integral in Theorem 5.11 of {{cite:a6e58cd1726641a71c4ef5cd14ac0c43b6bba093}} may be bounded by
{{formula:33f0e318-7b17-4757-9923-57fdaef0a482}}
| r | 53630a220cd69e264c459b677de6af8e |
Here, “resnet20” is a Hessian for the ResNet20 network {{cite:ab5d63e2af385f17090221fe00fd79867ff92ff3}} trained on the Cifar-10 dataset.
To apply the Lanczos algorithm to this example, we use a slightly modified version of PyHessian {{cite:9875d0ed2ae27b8be859748a6fa14735e59c56ee}}.
The “California” and “Erdos992” exa... | d | 01a0c5de749d3c9dbb0de504a7f3db92 |
Considering that the applicability of traditional two-equation eddy viscosity models such as the {{formula:3103c20d-8303-42c1-9052-3d4ea2edbefe}} and the {{formula:10fdd6fc-90bf-436e-a646-3f9bbe241cbc}} is limited depending on the flow (see {{cite:76f29e3904de37d7bf6034acf9ee85aa56b7ddba}}, {{cite:7d3a5d173bfd87fd2ac... | i | 8ecd04d4f8613334050ccc3d909ff18e |
with the initial conditions {{formula:256af270-9c6f-4cfc-b610-dec76a201964}}
and {{formula:7dd19cf4-f797-478c-a394-0b747bfc8793}} . We remark here that the first
order derivation {{formula:f7622cd2-303a-453d-82ea-6b5f7d2e7dca}} is not continuous at {{formula:b3191045-4c32-4c0e-9ec3-fa9531997de2}} ,
noticing {{formula... | m | d8fa2f84e6967226cad6a36f5f9792d4 |
Table REF illustrates the improved performance by visual clues, such as biLSTM-CRF vs. biLSTM-CRF with image and BERT vs. RpBERT.
The inputs of “biLSTM-CRF” and “biLSTM-CRF + BERT” are text only, while those of other models are text-image pairs.
“biLSTM-CRF w/ image at {{formula:3ccf8e53-cc5f-432e-b4e6-6bb0d7046a51}} ... | r | d1a043f11553d8feedc7a4a603828b54 |
As we see in Table REF , SLICER outperforms all other approaches in literature by a significant margin. Results of COLA and BYOL-A were borrowed from their original papers. SimCLR was proposed as the pre-training approach in {{cite:93047bfe2e1e1de77448520baeb92f8e711c5966}}. We attribute the gap in results from the ori... | r | 2449f95652d19d58724c4b8ffca92e46 |
Notice that the complex plane {{formula:cd5bb6f6-5623-42b8-8e55-49ba2518d663}} and compact Riemann surfaces punctured finite points satisfy the assumptions in Theorem REF . Moreover, the objects are analytically stable with respect to the same metric {{formula:db9f4ef2-9b01-4476-8a71-eebd4ffb05fa}} . For higher dimens... | r | 773000e5e006f9d4ed8b55d05cd71056 |
Most of the literature on streaming algorithms implicitly assumes that the stream updates do not depend on previous outputs of the algorithm or on the randomness produced by the algorithm.
This assumption may not be realistic in many situations: for example, when the data is chosen by a malicious adversary in response ... | i | b570ff26857bcc652a57ece934d0976b |
If the problem (REF ) - () has
a global solution, then the feedforward controllers are given by the
equation (). However, it seems challenging to show it
has a global solution even though it has a unique classical solution within some time from 0 to {{formula:8924d056-437d-40d9-b393-c7453ec668ff}} (see, e.g., {{cite:1... | d | 2045784e8e4aab12f1aa7ce0880c7e47 |
We attempt to simply combine methods for learning with label noise including Co-Teaching {{cite:7ecd9d4ce9c8f497f6a6c84d340eecc6499b5648}}, Co-teaching+ {{cite:28fd0149fb7c0888b914af038f992db195471dec}}, JoCoR {{cite:8a00e132dd5b7eaa1613d906e1715160195b014b}}, Co-Matching {{cite:67386ae4d851eacdb2386eece3e5d22ec9701909... | m | 0c3dcd086f606d20dc2faf589f669e61 |
General relativity can be considered in space-times of various
dimensions. Gravity is richer in higher dimensions as black-hole solutions develop non trivial properties in general
dimensions {{cite:b28f11433d64bded21a5a24e9efed2f90719023e}}, {{cite:2557a43f12a3ff7e651c77791a29beaa531a6203}}. It is therefore important t... | d | 0717f9e41e52242c92bfd2da2009989f |
which is the best reconstruction according to the chosen kernel. The kernel can also be interpreted from a Bayesian perspective; in fact, the same solution would be obtained by assuming that {{formula:7cc74057-006f-4b7c-bec9-d2ff9e1c128d}} is a Gaussian Process ({{cite:2916aefba0f4325ce392254bb1141090469a98cb}}) with ... | m | 14989d1d53a68c91b37e6697e2bb569f |
Therefore, we can simultaneously optimize {{formula:193afc11-f800-499c-a87c-759f77bfea0e}} with standard cross-entropy loss, and optimize {{formula:5607c5a9-29a9-4bb8-b2b8-df021388dc69}} with Stochastic Gradient Langevin Dynamics (SGLD), where gradients are taken with respect to {{formula:5f45b4d0-429b-4c91-9176-93d7... | m | c014fd1f4c3f71d11681647b454e1730 |
For general {{formula:1536ed28-b554-433d-b289-b41985eccf67}} , {{cite:c22dc34ac70c06e8e0992df6b025c1c5d3325a23}} derived the LSD of the sample covariance matrix whose Stieltjes transform {{formula:6198eb51-c249-4b3e-9adc-c6999dd0dfe0}} is given by the Marc̆enko-Pastur equation
{{formula:bac2d1fa-3fdd-4e1f-8540-55e47f7... | d | 7394a2c61b87723ab254966bf30d2bf8 |
Comparing Case III-LP with Case III-FT:
Figure REF compares the testing accuracy of Case III-LP and Case III-FT on STL10 dataset when {{formula:ded3dc1f-6671-4fb2-8757-b8c45d0b59fb}} decreases from {{formula:94b2974c-8a9b-4138-99a1-bc8d538d6b70}} (no privacy guarantee) to 0.2 (strong privacy guarantee). In these ex... | r | 353bf16c62bfb4f4786d540005d5fdd0 |
DA is divided into variational and sequential methods {{cite:0603e18cf26e7cafc18e30f6e183888171f78f13}}. In the variational approach, an adjoint model needs to be formulated and solved, which is a computationally expensive approach. Kalman filter as a sequential DA approach is a recursive algorithm that estimates the s... | i | 9460b3c0fc69b541c0e96cccdccf9279 |
By Young's inequality, we have {{formula:e5baddaf-8d0f-4b0d-8e01-48e17fd939cb}} for any {{formula:e39cd092-2c2d-48b3-885e-9cf24743adb6}} , and {{formula:61f83a6f-0b69-46bb-a8b5-de9eae3043bb}} with {{formula:c521fdba-b1ce-4f8a-85b3-389f6b163982}} (c.f., {{cite:71d8c55b139128a982b4cfbfb7966a1cf39cb783}}). Set {{formul... | r | f49c523f28eff4a59b140f290ba965c9 |
Basic GNN.
As Gilmer et al. {{cite:355cd36627bab358614699a5e614b9ff86357d66}} point out, the critical aspect of a basic GNN is that the benefit of neural message passing is to exchange and update messages between each node pair by using neural networks.
| m | ff7d050e69e5013c16a25c6bcbd8ccca |
We reproduce VKD, PKD, MiniLM, and RKD but MiniLM {{cite:f89956cb20271d78a4f6789c4031c022138309da}} performs KD on the pre-training stage.
For consistency with VKD, PKD, and FSD, we apply MiniLM method on the fine-tuning stage.
Previous work {{cite:8069f39df498809acbef735990cfc53bfae6180a}} uses 6-layers of BERT model ... | m | bb7d863ed87d5a12125f75ec91634cdc |
In this paper, B-splines are used to approximate {{formula:77cd7311-20e7-49bf-9dbd-2a5253d024db}} . For notational convenience, assume that without loss of generality, all B-splines of order {{formula:70450a9f-b366-43f6-a918-db851f11b68d}} are defined on an extended partition associated with a uniform partition of {{f... | m | eff6b4c15134bbb0b3eb005f6df4a6fb |
Our method of orthogonal correction is easy to adapt to different types of biased associations, such as the good-bad notions attached to different races {{cite:864617e74f9a36bf6aa70ad348f11d604682043d}}, {{cite:790dccd5ce71241498e94208d9fd129eec4d404c}} or religions {{cite:3379b3c009bc3e3791877e5dc1eaa0131f0d62d8}}, et... | d | 9093172b08f316e0a642b7426705c247 |
For paragraph generation, as shown in the upper part of Table REF , it is clear that models with a single LSTM decoder perform much worse than those with a hierarchical LSTM decoder. Note that the only difference between Ours-no-Attention and CNN-RNN {{cite:0320bf640ee1fd2e542cb9b48105124e81611c8b}} is that Ours-no-Att... | r | b458043f11e273b33f88465a38ebdf47 |
With Lemma 3 at hand, Theorem 1 follows now by a direct application {{cite:763e6b3c65550bc547f5e97e92fffb2a4527672e}}. {{formula:59a336ba-7dff-4845-8579-af0445da1f55}}
| r | bca2767020c7d4761206e5e8c1913947 |
Theorem 3.1 (Length distortion: Mean)
Consider a ReLU network of depth {{formula:c57dd68a-7a19-4cb9-bae5-e8fc15608423}} , input dimension {{formula:57cc0e4e-4a78-4e0e-b5bb-54c39bdb9cf0}} , output dimension {{formula:23690649-6c03-4bd7-be3f-83ad96506a01}} , and hidden layer widths {{formula:3e5bac8a-e952-4558-badb-5895... | r | b2327e893e93c9c506c7a1974d689513 |
In order to verify that predictions made by our architecture are based
on salient features relevant to the classification task, we utilize
Class Activation Maps (CAM, {{cite:60b9bfc8afeff7e715a645a9ced6c508b8f64cf6}}). CAMs highlight the most
important image areas in the model's classification process. Examples,
shown ... | d | 7288bcce3008d9a59110512df3fc397d |
Attention mechanism has greatly advanced the representation learning, and the Transformer {{cite:cf11631a2c1ad98c62d01bfb6a2878a48193e21f}}, {{cite:583e94f47edb797fcf6f666d498c2df6b4a30abe}}, {{cite:3fbe60df268a129fdc59350b276f80f90cfb64b5}}, {{cite:a77454d57be3a95725eafb8b4072b268821bbcba}} built upon self-attention h... | m | 06e38d02968eaa98e57b2b1add9cb63d |
Several clean and faithful corpora are collected to tackle the challenges from data infidelity.
TOTTO {{cite:0dde08822ea96bfd6f2144ecad7789c526eb4390}} is an open-domain faithful table-to-text dataset, where each sample includes a Wikipedia table with several highlighted cells and a description.
To ensure that targets ... | m | 82772501859b449189fafae11435240e |
Base Models.
We use two conventional conversation models, Seq2Seq and Transformer. We use the transformer as our base conversation model for all the following methods as it performs better than the seq2seq.
Fine-tune.
We fine-tune the transformer on the support set of the target speaker to obtain personalized models,... | m | 5ddfe5e7948b38486bf56df31e7b0184 |
Our analysis resolves an apparent contradiction in research on outlets that share misinformation. If false stories and clickbait generate greater engagement simply because they are more attractive to readers, as some studies suggest {{cite:f27b810f7337a3a7c5a43e978e2d2865e6758d55}}, then there is no motivation for a ne... | d | 880b7120857623b74789b485b810e3ab |
In practice, however, due to the complexity and the slow generation of EMRI waveforms computed using BH perturbation theory, almost all parameter-estimation studies done so far made use of approximated – but fast to generate – waveforms {{cite:6e168dcc98cc3a28d4aa4f0812aa12c6906f25ee}}, {{cite:8b8a3ab014ca984bbe4fcbeea... | i | d1bfb17f3f4eb022808830cbb0d5ee87 |
This result exemplifies the widely known fact that multi-fluid systems are intrinsically non-adiabatic. While dissipative physics is a customary feature in modelling the dynamics of real fluids such aspects are not present in the standard cosmological model. However, the phenomenology associated to cosmological bulk vi... | i | 88e83af4d44173f448dae32935ec1dc8 |
Concept-based methods aim to address interpretability in DNNs by extracting human-understandable concepts from a model's latent representations. Liu et al.{{cite:07a8efb007238079cb79eeae12ded09606b5121b}} propose a model distillation method based on unsupervised clustering that produces an Intrinsic (interpretable by d... | m | f401d7c4dfe61c047ee563e1361069b7 |
where the second equality holds because {{formula:a55e832b-e345-48b6-aa4e-c3020711afd0}} , and the third equality follows from the substitution {{formula:47830bae-e7e0-4033-ac5e-5ea2abbe02d0}} . We know from {{cite:f993d3ad017c9bebd32d2919877651ac88543224}} that the conjugate of the Burg entropy is given by
{{formula:... | r | dce121ff2109e271a6cbef77a8f346f2 |
The equilibrium state of a beam in plasma is difficult even to characterize analytically {{cite:c2479bb4c46bc24b2e7c45249523bd86cb42ff21}}, let alone analytically examine its response to perturbations.
So the only way to study beam resilience is through numerical simulations. For this, we use the quasistatic axisymmetr... | m | 7c77e59b08ed59f03e2e018e9e5bd9a5 |
Finding the maximum clique is a NP-hard problem {{cite:ba9214719534a46c13d22eda9c101ee76fbe85a5}}. We ran the maximum clique algorithm {{cite:6b1d6f6637f1c41c5c35cc18db518ec9b3f7bf53}}, {{cite:2e0e1da543eb605f8297409b406649cc9a9a235e}}, {{cite:fbc4722f4a5aaee141a5a8167295d384ebee00b6}}, {{cite:f2aedef203ae248116b50aa02... | m | 2cf9effb8332ad7afae6f411ec02a6e4 |
Using the fixed-point formulation of ADMM, (REF ) can be written as an unified {{formula:a2c67aaf-a7a5-4e62-ba29-59a107615ce2}} , let {{formula:2c50b410-266b-4f4a-b014-2a122ef47247}} , then {{formula:0841773c-8545-4d34-8596-d676c99dc2f5}} . We can obtain the convergence of (REF ), iff {{formula:b3f2852b-63b0-4b3c-bd73-... | m | e038669ac119c8a4dcbe074e047cfa28 |
The second ingredient entering our theory is the
requirement that the degrees of freedom in phase space
evolve in time satisfying the constraint determined by the constant thermodynamic temperature.
The temperature control of the classical degrees of freedom is achieved in silico
by means of the Nosé-Hoover chain algor... | i | 9d0f66954d9e44f2651b4fc58f21f322 |
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