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Although, surrogate gradient learning is one of the best direct learning algorithms for spiking neural networks {{cite:4e9cd9d8a77a77c0fefdfb44965650afe339657c}}, but it suffers from the challenges of backpropagation through time, especially for longer simulation times, including vanishing/exploding gradients and high ... | d | 5e0033aa432c7dff511f35f76d4bda5e |
Given such a setting, in this paper, we are interested in addressing two questions: given a large, unlabelled clinical database, (1) how do we extract attribute information from such unlabelled instances? and (2) how do we reliably search for and retrieve relevant instances? To address the former, the task of clusterin... | i | 141e1f1d007ba36b6efaaaecb4b23847 |
We conduct our experiment on a real-world dataset CIFAR10{{cite:ba1c9fa1f32223dc80e52987d3ad79f98796c534}}. The CIFAR10 dataset consists of 60,000 images in 10 classes, including 50,000 training images and 10,000 test images. Learning on the image dataset simulates the real edge computing scenarios such as traffic flow... | r | 6289d54346911481cff8b9ec50eb22d1 |
The algorithms we consider are deterministic. An interesting direction for
future research is to adapt SMR synchronizers to emulate asynchronous rounds, as
required by randomized consensus
algorithms {{cite:bb76fe20c84a611cb08abb6545231e560335c56d}}, {{cite:3613527891319d77b58e63835a700056dea31600}}, {{cite:709fa285f62... | d | b7ac54692d4e761a96bc5b7ea8fb568d |
These images are then assessed by a scorer. Specifically, the fitness of an image is calculated by either (a) the loss against a specific ImageNet class under a trained MobileNetV3 model {{cite:f085fc63597cb63bda7686190126fbbea25e050a}}, or (b) the similarity to a caption under the CLIP model {{cite:2e80092c357ad30056b... | m | 66542435fb91fba63e9b9c7aa4beaddd |
We compare the proposed FL scheme with the FL FDMA scheme with equal bandwidth {{formula:32a39262-f6a3-42e7-9d5b-1b848f173928}} (labelled as `EB-FDMA'),
the FL FDMA scheme with fixed local accuracy {{formula:6677f708-8a26-4f52-aa4a-b647361e774c}} (labelled as `FE-FDMA'), and the FL time division multiple access (TDMA... | r | e64eeed68ffcf58cc06d727061956635 |
Next, Theorem REF gives a lower Lipschitz bound for max filter banks when {{formula:3d89ac1a-871a-44e2-8648-67e31a2d93fa}} is finite.
Furthermore, by Lemma REF , this bound is optimal for several choices of {{formula:13396456-d04d-404e-807e-00937ef23d8b}} .
It would be interesting to determine a lower Lipschitz bound... | d | 7777f859e686e24e574f5dedf9213bc4 |
Recent developments show that, Transformer {{cite:279e33a441d30c3c39b93fe47d3655846c565716}} based pre-trained language models like BERT {{cite:02a3e949e71fa76306c9d2969795b711ae99b6e2}}, RoBERTa {{cite:b793e514b16da38109b5c0e8cebe2ec7d3ab3db8}}, ALBERT {{cite:65a4ca3e96dae13c033fe60d5d98d2133362f41f}}, and DeBERTa {{c... | r | 3cd42f4b4ac75f90b595a31e636ed924 |
We are interested in solving the nonlinear system of equations. The nonlinear complementarity problem, is identified as an important mathematical programming problem can be converted into nonlinear system of equations.The idea of nonlinear complementarity problem is based on the concept of linear complementarity proble... | i | 0f280ef719bd3991db46aefcc4fbcd5f |
Systems are compared in terms of BLEU {{cite:4256c75cdd9db8ddbc80c8759b2db4f54c472e52}} (as implemented in multi-bleu.perl A script from the Moses SMT toolkithttp://www.statmt.org/moses) and TER {{cite:ed5158c3ae432897e0cc302fa50442bac9c55524}} scores, on the single references of the official IWSLT test sets.
| m | 4f4a1e8125291b61b7b06dbd8c251ad5 |
Several of our experimental outcomes were unexpected and need additional investigation. Our results for the convolutional model were initially very promising, so why, at a high dimensionality did our attempt to regularize the network fail, when it succeeded at a low dimensionality? While one might assume that dropout w... | d | dbb7e70c98c4a5355a7b3ad8d844fc48 |
In this section, we summarize our experimental analysis, resulting from more than 500 experiments. In our experiments, we primarily explore the effect of temporal misalignment on GPT2 {{cite:5f22cc014e77603d8a89891f282935750d6b2021}}, a PLM often used for generation.In our preliminary results, we found that BERT, RoBER... | r | eee30400517759afebf9d96aa1778882 |
In this paper, we build a novel pessimism-based Bayesian learning framework for offline optimal DTRs. We propose to combine the pessimism principle with Thompson sampling and
Bayesian machine learning to optimize the degree of pessimism. Theoretically, we derive the upper bound for the regret of the proposed method and... | d | 1fb154f66e28686c678b1bb9e055eb6a |
The recent strategies of ameliorating CNN efficiency mostly focus on compressing models and accelerating inference without significantly sacrificing their accuracy performance. Among adopted methods, progressive pruning appears to be an outstanding one where a deep neural net is trained, then pruned, and then fine tune... | m | 5cdf93f4be08cdb66e29f47be6662b7d |
In the remainder of this chapter, we categorize meta-learning techniques based on the type of meta-data they leverage, from the most general to the most task-specific. First, in Section , we discuss how to learn purely from model evaluations. These techniques can be used to recommend generally useful configurations and... | i | c1849cf3888c1102fe8f3fa5f9c9855e |
CE is crucial for a number of downstream applications, including, e.g., language understanding, ontology population, semantic search, and question answering; it is also the key to entity linking {{cite:b7fdd573b7813a877a7e0fd93fc252d3b68bbb04}}. In generic open domain subject-neutral discourse across different (potenti... | i | 0c90770d17dcdd177dfdd052eb5ba7c9 |
In the literature, the simple Gradient {{cite:d36d5d9056f9b2783ad7bfdd9986aa6fabda3cb3}} method often yields noisy saliency maps {{cite:e22c9f627fd54d0f12e7bda73ce2ce657141f658}}, {{cite:eb208579d820ae88903ca5e3d5e8eb5c87985c14}}, {{cite:8a39e84c5f9422470010b7a9e7b6e03e297c4b2e}} on vanilla CNNs and is thus regarded as... | m | bb6ca8f27b15c4d7efc12bf1269b74b7 |
These issues motivated recent efforts in the nonlinear filtering literature to develop numerical algorithms
based on a controlled system of interacting particles to approximate the posterior
distribution {{cite:1626dea15345bd1e822e18b814e4ec3d8c6498cd}}, {{cite:e2cf31824e3f7643f2b63b07b48304bf0f7de8bb}}, {{cite:e8fb917... | i | be80e185f6be82fc7f01fb70df1a9906 |
Combining aforementioned observations, we concluded that the utilization of deeper layers are a suitable estimation of EMC. It can be easily calculated, which makes it extremely useful in practice. Finally, we also provide the empirical proof that performing rolling back on final layers does in fact allow models to rev... | d | 50362e13bef9de8a70bc0368d5fadbd4 |
Algorithmic information theory has been used in causal modeling(see e.g. {{cite:3283b6b083a09fc8ea4165ff434a23ee621ffdef}}).It is also worth to mention that, broadly speaking, the information accounts of causality can also facilitate interpretation of existing widely used causal propositions. For example, regarding bac... | d | ebd2a4445e0b532db8ffe4aca98f2e55 |
Some important aspects concerning the inversion formula remain to be clarified. Firstly, we did not place an upper bound on {{formula:622a0b70-fe7e-4878-959b-eafc2eeef833}} . Theories whose correlators are not polynomially bounded in the asymptotic region mentioned earlier may not display Regge trajectories. It would b... | d | d990ef8793910e426a9aab942da1f4f0 |
Many systematic studies of dark clouds and low-mass protostellar cores have shown the HNC/HCN ratio to be close to unity {{cite:cccf913c192e438348e62745a71b13d31a58e42e}}, {{cite:f86af0845a2a186fc37ab3ca6026d1f2a7342041}}, {{cite:dc9c2aa928006192d58d68547c745995d9f58d62}}. No difference is observed between the values m... | i | 673f2ffe9d10d0aeefcd5775990646cf |
Table IV shows the result of our model for sentiment classification against other models. We compare our model performance with the approaches of {{cite:03088432ee2d626baa295f073720ac5040dea03c}}{{cite:6e7258e597f48c433d5867bf974962ffd02978f0}} on STS Corpus. {{cite:03088432ee2d626baa295f073720ac5040dea03c}} reported t... | r | 931dbc2412fcd5d88d01b71a9e6bd831 |
where Please see Appendix: for a derivation using the loop number density obtained from the Velocity-dependent One-Scale (VOS) model{{cite:51943941f842ce865dbc85492edcd7dcf2b9e03a}}, {{cite:63aa375b6537091ba981c08347e12a77ab318006}}, {{cite:4f5ace063d6308507e708bba3b8283b6553cf559}}.
{{formula:271a8004-ca2b-438c-ab53-1... | d | 2881bdc949d2bef02a8852dbbc3cde5d |
The above corollary can be viewed as an extension of Theorem 1
in {{cite:7b2bced6bf3daa51cf9115df21d3e99f9addf06e}},
which established that {{formula:46c5a5ef-1fef-49cd-a2b4-53acb1628203}}
when {{formula:02eeea77-0b0a-401b-bb30-9a3d9629ce5d}} is equally spaced
over a bounded interval {{formula:2681c7ad-ca45-456c-98d6... | m | 59a44a2450796afd839ebb1b97627b4a |
Towards this end, we proposed an explainable machine learning model to predict driver fatigue using XGBoost (eXtreme Gradient Boosting) {{cite:b3da41c4a689f04128944a40963640699e42fd21}} and SHAP (SHapley Additive exPlanations) {{cite:7dac22b7367ac263ee9b09a5ffd3ce217119d8f8}}, {{cite:643d1cc83bf773518904fe37f733b3a49ab... | i | a948048d03c8390d4d2dca628db2898b |
models accuracies drop on {{formula:0437e914-d41a-49d5-9ba2-d2f3a24590ec}} and {{formula:fe771985-0da5-4a40-85a9-997880981409}} , suggesting that all three results together should be used to characterize the model, and not any single one of them.
All our models are significantly worse than the human performance ({{f... | d | a70f9f9f68f2c5e29b7b398a8bb19653 |
Y. Lin et al. suggest a novel multi-task framework called Seg4Reg to generate precise spinal curvature estimation. This new approach proposes an automated method for the prediction of Cobb angles from X-ray scans. Its process contains two deep neural networks respectively concentrating on segmentation and regression ( ... | m | cbb76fd25994e32b6543a0d75817d0d7 |
[leftmargin=]
Temperature {{cite:b3a27fc9b31e48384898b518f1c16fb4cdef1869}}: control the randomness of predictions by dividing the logits by t before applying softmax
Top-k {{cite:143a0e3c2941c9519d1d5224088235d36460896d}}: filter the k most likely next words and redistribute the probability mass
Top-p {{cite:f19986c... | m | 35312f42a371c60d30b11e70913653b1 |
In addition, spacecraft such as Solar Orbiter may also be able to reveal this newly identified sub-inertial range in magnetic energy spectra through remote sensing observations. Recently, Extreme Ultraviolet Imager (EUI) onboard Solar Orbiter observed transient small-scale brightenings prevalent in the corona of the qu... | d | 690f772cfef0a768c02b240c72e588c1 |
We employ a deep neural network for the experiments in morphological inflection.
This consists of an attentional sequence-to-sequence model, as described in {{cite:1974c20171e41ca0fa3da4c3f2f8201fcb76bce9}}.Available at https://github.com/bjerva/sigmorphon2017.In the SIGMORPHON shared task, this team placed as the 4th ... | m | 279147536af8c20b178d3ad091238d49 |
A general GNN framework mainly consists of two key steps: neighborhood aggregation and feature transformation. In neighborhood aggregation, a given node first aggregates (such as sum, mean and pooling) its neighbors, followed by a linear mapping or a multi-layer perceptrons (MLPs) in the feature transformation step to ... | i | 80e7c77fea8fe211cc49e9bd1061849e |
Our approach is to present briefly the single-record SSI data equation and approach in Section before posing the multi-record formulation in Section , drawing on their similarity. This multiple-segment algorithm is an extension of that from {{cite:1025de44370e6c1bb4b6f987d4bb6225859db3c2}}, {{cite:de486d5a939bc89c3fd6... | i | 1d1a43531f7c699362c8ed76638061e7 |
By Table REF , the tangential circle packing is a special case of Thurston's circle packing and
Thurston's circle packing is a special case of inversive distance circle packing.
For simplicity, we unify all these three types of circle packings as inversive distance circle packing in the following.
By Table REF again, ... | r | 77fa499a7e746dfcff5f2191c50eb2a3 |
NGC 5322 is the foremost member of a galaxy group with velocity dispersion of 169 km s{{formula:3bde80d3-0b47-4322-9f0c-75f0f1de379f}} with 21 members {{cite:3e896896b346dcdc672ab2779ca4b2f579f4cb9e}}.
In a study of several galaxy groups using XMM-Newton, {{cite:16e8b2ee7e5994e40c3fd641967ef68c09756faf}} found that NG... | d | 7737b98b8b5b8a15807ca7f8172ca80c |
Moving object segmentation (MOS) is a fundamental task for autonomous vehicles as it separates the actually moving objects such as driving cars and pedestrians from static or non-moving objects such as buildings, parked cars, etc. This is an important processing step needed in many applications, such as predicting the ... | i | 837d475487a7316e0b36e43730d071d7 |
In the following we describe in more detail how the {{formula:8790779b-f5c0-44d3-b6a2-a1a00b803781}} SCF calculations and the linear-response TDDFT calculations are carried out. For all molecules, calculations were carried out for the relaxed structure obtained using the SCAN functional with the default tight basis set... | m | d32396f156315a82415cc1c78c30edfc |
Numerical simulations of the collapse of a rotating, self-gravitating,
isolated cloud cores, began to be performed many years
ago; see {{cite:d57ca0b63d865db988aa9ca23e40e8b3f0337106}}, {{cite:e8f1695f717d6e1ef30df7a7283cbab0a68b4c24}}. One of the
classic models of binary formation is based on the
collapse of a rotatin... | i | 19a8d96bae546b8bdd9b481ca8ed8355 |
This inducing-point approach is initially proposed in the deterministic training conditional (DTC) approximation {{cite:04d81d5ce5d7aac5b92791179a46bdd9ee48cc5c}}, and then it has been fully studied in several works. {{cite:0701af71a6d645be5d1fe1a257084711911727b0}} provides a unifying view for the approximation of GP ... | i | 32632b861e36c0dd3f863ec51fa83528 |
For fairness, we compare DirectCopy to the gradient-based baseline which uses the same-sized linear predictor as ours. At 100-epoch, this baseline achieves 68.6 top-1 accuracy, which is already significantly higher than BYOL with two-layer predictors reported in the literature (e.g., {{cite:ec283fd3abd2cfc154a0a9182efe... | r | 263f6be8406634b4f3d231bb311c877c |
One challenge of leveraging interpretable machine learning techniques is that current interpretable models are usually developed for supervised learning models {{cite:e36e9589dea82a9fa15eeda3ee7854776580fb33}}. For the counterfactual explanation approach, a classifier is usually trained to distinguish the original samp... | i | d3bbe87d86dd1d32cba0568a5127db98 |
Repeated data induces a strong double-descent phenomenon {{cite:a7b3b88b0d450ef6c9f275aaf7de6d0ea6339db5}}, {{cite:e20c953d1872c5b7b6a64646714480a99acee48d}}, {{cite:6a36be747a301e4dbe1b73c1e9f216a09753d2f1}}, in which data repeated a few times does not cause much damage to language model performance, data repeated ve... | r | c436ddd81a31e5c642ec5ac77f5768d4 |
Data Set Description.
We used Pushshift {{cite:7ce7c43ab1a6673061e2cb3b005b5f6eaec02775}} to extract Reddit data. In particular, we extracted all submissions that contained the word “Cuba” from 07/07/2021 to 07/17/2021, covering a few days before the protests started on July 11th and the first week of the protests, all... | m | db91adca9af87267bb073b6bf6d98df9 |
The proposed architecture is described in fig:architecture which is based on TransTrack {{cite:a6f9854cde37893d27769568d3d9ba0d0dcfe341}} with several improvements to reduce the computational complexity and model size. On a high-level, we generate a set of bounding boxes per objects of interests in each frame. These bo... | m | 55d50e5437cbe6b33fc81f7b73694c4e |
In recent years, on-policy methods have also considered non-parametric target policies obtained by solving policy optimization problems similar to those used in TRPO and PPO. These target policies are then projected back onto the space of parameterized policies. {{cite:ea9df1c2b649078299e965bed3a7953a123d3b04}} conside... | m | a4d6b1311a274a2c52721460f36bfa2a |
The GA is now amongst several other very large LSS discoveries with sizes
that exceed the theoretical upper-limit scale of homogeneity of
{{cite:692c7fa1370256dcce0e8af9cb01943537b40d43}}. In Table REF , we listed some of the very
large LSSs, and also some of the reported CMB anomalies. In standard
cosmology we expect ... | d | 177c7e62c4c7a60ab05396b3233ee0c1 |
Is there any advantage of one approach over the other?
Our experiments show that for a deep network of the same size, invariant representation learning can be just as effective (Tab. REF ).
However, invariant learning is conceptually simpler and scales better than equivariance approaches, as the latter maintains high-r... | d | cb0be73b9d0a03e846c9796d6b47364f |
Attention Mechanisms.
To address this problem, we add attention mechanisms (AM) into our network.
AM was first applied to NLP problem, and then it has achieved great success in CNNs recently, which can help networks to focus on key objects and take advantage of contextual information {{cite:ef63b246db367f1398a10030f22... | m | c82851b67f7744c333d547dcc6a0fefd |
As shown in Table REF , PNODE outperforms all of our baselines by atleast 27%, 48%, 70% and 5% on clean, FGSM, PGD and SMIA attacks respectively for BCV in-domain Liver setting. While PNODE provides a better defense against the adversarial attacks, it also outperforms the baselines for clean samples. This indicates tha... | r | 5e8456b84a0c969a11cbaa37b7006cf2 |
The enhanced impact flux by ejected particles may persist on short time scales. Particles most likely to impact Gault have intersection speeds below 0.2 km s{{formula:683a24e1-f2a9-46c5-8d1a-17d6257e7959}} , regardless of when they are ejected. This encounter speed of a single particle may not be high enough to induce ... | d | 1928a52925d24c4b8e6a8e8018d03029 |
As we mentioned before, a larger batch size in architecture searching phrase is prone to produce a better result. Our method achieves 2.81% test error on CIFAR10 with the same batch size as DARTS and GDAS (B=64). We obtain 2.61% with same batch size as P-DARTS {{cite:6d8f79821cd1534f7b917cdd402dd805f6749884}} (B=96). O... | r | 1b7f4a4a031b9c52be7cf5227cfe0eeb |
{{formula:c646e9e9-05d7-4063-92f3-3c517f44b6b7}} //Assign rank using non-dominated sorting {{cite:8a87a5733554b3308bca59ba83f0c122f490e1a0}}.
| m | d6bc7a4a1ed79902dbb914fb1d91e577 |
When analysing a complex probability distribution or facing an unsolvable integration
problem, as in most of Bayesian inference, Monte Carlo methods on a large
variety of solutions, mostly based on the ability to simulate a sequence of random
variables and subsequently call for the law of large numbers. Techniques base... | i | 895241eac40dab512b8c051f678f646d |
In comparison, our results in the same order are 2.8 %, 2.8%, 3.3%, 1.2%, 5.9%, 4.5%.
We note that these results are in the same ballpark as those of {{cite:8d3821a0d27b01fb2f300010d323b750f77a39c6}},
although there are differences due to the detailed assumptions of each setting.
| r | 87ba436956ef6dc42f36ba672d55d023 |
The effects of rotation and enhanced diffusion were studied by {{cite:4862d49a0444c69341c18527ca44dcd05e4389f0}}, where the value of
multiplier ({{formula:27b012f8-52e6-4226-9942-7fc62869bf0b}} ) was larger than or equal to {{formula:af671ad1-22ee-4d7a-8463-6d9cf07480c3}} and OPAL opacity tables constructed in accorda... | d | 46286dc9e000321e45b58e8fc74b90b0 |
More importantly, we propose hydrodynamic entropy to quantify disorder in Euler turbulence. We observe that the hydrodynamic entropy of 3D Euler turbulence increases monotonically with time, whereas it decreases for 2D Euler turbulence. Thus, 2D Euler turbulence is a unique isolated system that exhibits evolution form ... | d | 16f19653c2fbf9e6fe3f02c0b5186824 |
For VAEs, neither data for the “zero-shot” category nor for the other categories are (to the knowlege of the authors) available for the standard benchmarks we used. A reason may be that (like for the BDGAN) very intricate DNNs as well as sophisticated sampling and training methods are required to be competitive. Experi... | d | a9766e1f202a344829538bdc2a0315c6 |
We envisage that the approaches developed here could be useful for a variety of other problems. The thinning algorithm by Lewis {{cite:fc44abd09daab954e0790185f1d4ffe267b36033}} is somewhat undervalued in our opinion and can be modified for systems beyond those involving single-species Poisson processes for which it wa... | d | 9c484cebd1b66a442860b6a1bfc811c1 |
Inspired by Proposition REF , we aim to estimate the cost function in (REF ) and then minimize it over the shrinkage factors. This may be achieved using different strategies, e.g., {{cite:006b772170218c030888d1d2e2b326004d59739e}}. In this paper, we apply the LOOCV strategy {{cite:cfb76cd32e799f71fb2898277152550c664d6e... | m | 668f41ecf0eec36560c77dd7dc55b25d |
To remove Martian dust storms with deep learning, the first key step is to synthesize dusty images from clean ones to supervise the learning process. In particular, the data synthesis process must be fully controllable without changing any other image content except adding dust storms, which prevent us from using unsup... | m | 30e6b087c978f0103d7e12337e742b93 |
The {{formula:21252b42-8ccd-4b67-b3c9-d4212f5e7be3}} Minkowski problem. The {{formula:21eb03cc-1178-47b5-8ed0-770c654f2dc6}} Minkowski problem is one of the central problems in contemporary convex geometric analysis. The classical Minkowski problem states that: given a finite Borel measure {{formula:20ca1f9e-9974-463... | i | 2c850100edd3ed6802dc684bbe46f0c4 |
Simple Baselines: We construct simple baseline classifiers {{cite:0459be861e49e057e1ca42b1b4d65df18c8fa443}}: Logistic Regression (LR) and Support Vector Machines (SVM). The input to these models are constructed by aggregating the 300-dimensional word embeddings of words in each review.
CNN: A standard Convolutional ... | r | aa5433498eebf0df4a85c4707689926a |
We note that the performance of all methods can be further improved using additional structural information as discussed in {{cite:a42c077eaf82344fd08115da7a82225081cffdf5}} and falsification techniques in training as in {{cite:928a532e63e8e45f79d50b0d3382c362d4e9d21b}}. We leave these improvements to future investigat... | m | adf0d2924ac53b3704b52e9ca4ef3f3f |
To the best of our knowledge, this work is the first of its type to comprehensively cover the most popular deep learning methods in NLP research today We intend to update this article with time as and when significant advances are proposed and used by the community.
The work by {{cite:8cbe5e281aa4efff214fcd82d4c04ba068... | i | 132fe39cbbecdd3d2507951fc320a6a3 |
Segmentation CNN: The segmentation CNN ({{formula:4714c26b-c76c-4904-9162-6cf1355b57b8}} ) used is an encoder-decoder like architecture {{cite:c9dfbf22e567de6343b30138ab16a297128530fe}} with the encoder having layer definitions similar to that of VGG11 {{cite:af16b45dc77ff2a7934d06a60ab3609698886503}}. Concatenation of... | m | c700a195ee6d7e0faf24d1ee905ce785 |
First of all, the case {{formula:7dde837e-059a-4983-be66-647299f3707d}} is treated (together with the unlabelled case) in {{cite:00d34b4fb471a84c16855cf36e9767e3f0c7d58b}}. There it is shown that in the subexponential setting both {{formula:c1c3854f-5b1d-47be-86d8-6b95e78ecf4c}} and {{formula:8e3a7181-1b87-40d9-8fa1-... | d | fc6cab3df81ad68765f1c3c6a300c922 |
In DynLab, each rigid body (object) is now semantically meaningful, so apart from the 4 baseline methods from subsec:exp:sapien, we additionally compare to the following two alternatives:
(5) InstSeg (Instance Segmentation): We take the state-of-the-art indoor semantic instance module PointGroup {{cite:ed6c27f2230016f... | r | 38325c898dc2ae6db682bc80d2434093 |
In this paper we have focused on the simplest case of Schwarzschild-AdS black holes, but orbits should exist in more general situations as well. It would be interesting to generalize our analysis to other gravitational solutions, such as charged, rotating, extremal, and supersymmetric black holes. On the boundary, thi... | d | 04b13c9b68162cdd11bd3fdf3b1a24cb |
Linear Multi-Step (LMS). These methods rely on the approximations generated at the past {{formula:02193953-9a9f-41b2-b95e-e840966e89b2}} points, that is
{{formula:09c3333f-527b-4e40-8fc8-9c273b7a9ea9}} and the first-order derivatives at those points to obtain an approximation to {{formula:c769e2c4-39da-476d-9dae-a9e... | m | c7be8788a2bdc611892ae9f3b6bbefea |
Input to all the models are word vectorshttps://code.google.com/archive/p/word2vec/ {{cite:1c27f98fd19318daa937905307d55b92b3f0e2e7}}.
The evaluation on amazon reviews shows how well the single-task (ST) model perform when compared to the existing top-performing domain adaptation models on benchmark dataset. Table REF... | r | 6178449829835f3e0f6f3a57eb0c490f |
In this paper, we present Mixer-TTS, a non-autoregressive model for text to mel-spectrogram synthesis. The model backbone is based on the MLP-Mixer {{cite:b42df816944fe7fc5fa5fe0b206d6ad774b153ce}} architecture from computer vision adapted for speech. The new backbone makes the model significantly smaller and faster th... | i | 632a457728ff76fb3a5b3d223fbac355 |
From Table REF we can find that the branching ratios of {{formula:0896ffa8-a4a8-4e19-acd8-92a91e7ec2c9}} decays fall in {{formula:4d3480bb-07ea-4f5c-919a-0429de9e944f}} order.
The experimental data for the branching ratios of the decays {{formula:7d5ee2af-b698-45b9-8963-6a5fbc185e51}} , which are given as
{{formula:... | r | 6863722a0862a0d52f6e25638c994f79 |
Many pruning techniques have been researched to trade-off model quality and computation efficiency {{cite:11c6dc2c5c0592d6bc489bd1b47aa62b54203233}}, {{cite:aec2e02b86a813a9e0047f7a8febc73521a04eae}}, {{cite:457f27f30bad5baac77b8f9ee5a23eac5219d10e}}, {{cite:618ab496b5531a92d29869cc69379a7d8b20e3b9}}, {{cite:366650d181... | i | 4cfba5501d5011400eb4b9af8381911c |
Our group importance method may be relevant in a wide range of applications beyond risk adjustment, although we caution researchers to first carefully consider the context before attempting to identify potentially marginalized groups. In some settings, identifying groups could actively cause harm, e.g., may involve col... | d | 7abbef59967c17bdc678447f8a9c7dad |
RelGAN {{cite:a52c2c255406678303b408e5795e245a6299a3f0}}, DLOW {{cite:69ea880f9523768be48d79b1084615351a68bf53}}, BicycleGAN {{cite:7bfbd9f0a5bed4f37be7e771d3aaa0655e9469ed}} and AugCGAN {{cite:a75ab2eaeb94b1e93c9fee7dea845a320b5984d1}} seeks to study consistency on unseen transitions. However, the generalization abili... | d | 294bd7ac3926f1d9393fd33f15ba7019 |
For the graphite structure, the experimental evidence obtained in the last years suggests
that high temperature superconductivity exists at certain interfaces or
interface regions within the usual Bernal structure
although the structure of the superconducting
regions remains unknown.
One can
further speculate that due ... | d | c89046d254d38879591bbfa15bdbccc8 |
A classic reference on the Monte Carlo method
which includes discussion of several variance-reduction techniques is {{cite:bae6dd2e1f5f9071260f1be1008a9d752fad31f6}}. The
chapter notes {{cite:bcc677c433042359e6d1da58e85e1fba4c0360be}} give a comparison of Monte Carlo and Importance Sampling with examples. The paper {{c... | d | 5b486a8263cec7744c85925977846d91 |
In this study, we focused on estimating HTE using an ML method with an interpretable model to capture the relationship between the characteristics of the individual and the effect of treatment. However, the models of most previous ML methods are black boxes, making the relationship between the characteristics of the in... | i | 73c9f04a264e5276ee79ad4e8259471c |
In order to scale learning for combinatorial problems, we ask: how much can we learn from unlabelled combinatorial instances?
In this work, we consider a contrastive learning approach, which begins by creating multiple “views” of every unlabelled instance, a process called augmentation. An encoder is trained to maximiz... | i | a49c4f712d33378b4ff1c0bec0397134 |
P r o o f.
We follow the standard argument of Ruzsa see, e.g., {{cite:7035595a26594818cf5a8e367a18f02a16cd11de}}. In other words, we need to estimate the size of {{formula:642b0376-3455-49bb-9feb-3c68e4c3812e}} .
In terms of {{formula:33872e5a-fef6-4455-a9de-c7a421eaf5a3}} it gives us
(consult the proof of Lemma REF ... | r | 713bbd30689a525150125e5f6d927f59 |
This paper addresses problems that have a long history, going back to seminal contributions by Kondrat'ev {{cite:87e966cbc6ff607c45c1a29de544434ed7f3ebe7}}, who investigated Fredholm solvability of classical boundary value problems in domains with isolated conical singularities on the boundary, and Cheeger {{cite:30a55... | i | 59f36ff4ef1a9e6b3476b80cd6820410 |
We take into account the randomness of the {{formula:fddd8a70-62c4-4581-954a-42900d889c12}} sites distribution in the KAM using a DMFT/CPA approach {{cite:b795eba1a217b475e9cb00711ab70f93b312c5c5}}, {{cite:b8044f0b461c23a952abdad20849523e457664d8}}.
First, the action related with the KAM Hamiltonian Eq. (REF ) is expr... | m | 648e68938c9ea52553f3eb78fda83fb0 |
In recent years, with the development of technology, the research on
networks has shifted away from the analysis of single small graphs and the
properties of individual vertices or edges within such graphs to
consideration of large-scale statistical properties of complex networks.
Newman {{cite:90ed16b7ebd70ab83f0c... | r | c3fb30978e2866a51a5ef55caf4f523b |
At present, a quantifiable generalized metric for the visual quality assessment of images is an open problem in computer vision. However, in the previously proposed pose transfer algorithms {{cite:a697dd698a9655c9ec99a5778b7dd66a76ff7d1e}}, {{cite:c95cd184aec3c470f52c1740fe305f7f6af550ae}}, {{cite:03dba14767f326ac4e5bd... | r | a6fc713d57786a9bbc10e752de2f223e |
A second example of a {{formula:f20365c7-c3fc-4cca-aeed-aed1a8cdea6a}} , identified in {{cite:e74e4ddea0450831d21ac16ef1dea2918acf31b8}}, is finding a “not-all-equal” assignment to a monotone 3-CNF formula given that a “1-in-3” assignment is promised to exist; i.e., given a 3-CNF formula with positive literals only and... | i | c92834b2fca035e553c619bafa38eb02 |
As shown in Table REF , MAFormer-S with only 23M parameters can achieve a top-1 accuracy of {{formula:11738b2f-4531-41f4-9446-76f7062b4d6c}} % on ImageNet-1k. Increasing the embedding dimension and network depth can further boost the performance. Table REF shows in details that MAFormer outperforms the previous state-... | r | 8fa1999894e1d2abe9c5bc8299d8ca14 |
apostle uses
the same hydrodynamics and galaxy formations prescriptions as the eagle
project {{cite:fc3b083416ca02320cbdd1d54a32200d8c90f770}}, {{cite:b2c2aa60e78be7ca07eb6440022ff20f754c0fcf}}—specifically, the model labelled “Ref” by {{cite:fc3b083416ca02320cbdd1d54a32200d8c90f770}}. The hydrodynamics are solved usin... | m | 9529e6918eecb29715a8b5f0b875d450 |
Self attention-based models suffer from the complexity of {{formula:8d86fc9b-026d-4ba1-b15d-e536d40eb254}} , where {{formula:6c7aeff1-5ad9-496a-a9a0-3c982c10de1c}} is the sequence length, and {{formula:4ee5d5a1-2b38-4540-8e80-d55a0640f1f7}} is the dimension of hidden representation, making it hard to encode extremely... | d | 7f95856583fe4eeb721620a13402c0b5 |
It can be shown {{cite:4f82440fef749d78cb101eacf2a71d6f0bd332fd}}, {{cite:79a3331720aff493825512b5a59d27bd5145d26a}}, {{cite:c5ae31f763d9f528e44f9e3978c8ce20240af256}} that the optimal control law/policy is the one that maximizes the Hamiltonian {{formula:9c4aca66-4fe5-4428-a01b-2122dd5a5422}} of the system, defined a... | m | 177196ff94e2afea1a333765d2e4ecad |
Long Short-Term Memory (LSTM): As an effective model used for time series data, LSTM {{cite:89166609df7ec4f08d7f5e25821900993bdb4f4f}} has been widely utilized in stock prediction {{cite:02d0c1b354aa1f2c0dcdcd5144af5c9786da0ad3}}, {{cite:cf20f7a8b1292a08a3c240fd481b304b70f9528d}} that achieves great performance.
| m | 3b4ccbc50683c51b1bb3d1f5b33db9c1 |
We next investigate why contrastive approaches show superior transferability by analyzing the similarity between hidden representations, intra-class separation, and robustness to image corruption. We find that contrastive approaches learn more low-level and mid-level information that can be easily adapted to a differen... | i | 38d6de9873efb41f9b6033223f5afe58 |
Even among mathematicians it has been firmly believed that
the quantization must necessarily smear out the singularities
of the classical Einstein's general relativity {{cite:7a154b0d931dc69913fb1d58200a9190cab7b041}}.
In this sense,
before any return to the quantum Big Bang hypothesis
it was necessary to wait for
a re... | d | 236d02884b244be1e75486042da492f6 |
which is {{formula:e6e0ba7d-4985-4df2-a808-525ada44b15d}} for the spectral index {{formula:48aedbca-83f4-48ef-9c67-dfbe970cbf20}} and {{formula:ce02018d-55dc-4b2c-9e02-bea1a27ab0bb}} for the rigidity cutoff. No far outliers are found and the small widening of the distribution for larger values of {{formula:929bd140-... | r | 47d9209a34d35b76a2a6ea15291de285 |
We consider a BS with {{formula:2d72a7c4-6a93-4741-9c0e-07f1382583e1}} transmit antennas and an RIS with {{formula:9736bc4c-6d01-482c-b6f6-938f34484f9e}} elements for serving {{formula:ab92b5f2-5b2d-48d1-af32-5b34045a7aa4}} users. For the wideband THz communication, the transceivers work at 100 GHz frequency with {{... | r | a06e46322c9887663a058a200b476f9d |
It is important to note that each of the two states on the left and right side are exactly thermal and the thermality in each copy arises due to the entanglement with the other one. In the language of AdS/CFT correspondence {{cite:51b9e6b523cbdf8dba056f4d29bca95443f37ec2}}, thermofield double state is dual to the etern... | i | 15025e73c2370c57d8601e8b644a202d |
Since the derivation of the method does not depend on the details of the model, but only on that its equilibrium distribution is of maximum locational entropy with moment constraints, the maxent closure may be useful beyond spatial ecology where unclosed hierarchies for particle distribution functions are also commonly... | d | 76f118b63277c07088ed5a60d2d6f3ff |
Very recently this issue was generalized to Taub-NUT/Bolt-AdS spaces
in {{cite:c18b3df4438a6895bfa9768a68975d926b467df3}}, {{cite:e75f354133c30b1d83a6cb9fbae24bc639029788}} and to Kerr-Bolt-AdS
spaces in {{cite:0b767a612939da47e6521a1ed106d7ae78930706}}. Interestingly, they found the
thermodynamic volume in Taub-NUT-Ad... | i | fb8446ad583904905443dbcd49329ac3 |
Proposition REF also gives a way to explicitly control both sampling and labelling bias:
i.e.,
operate on a subset of labels
(for computational efficiency),
but also
ensure good performance on rare labels
(to ensure “fairness” across classes).
Given a target set of label margins {{formula:19c54be7-13e1-4728-8cda-b3e7d... | d | 2ac4313ee4dba730e9d26ffd1d11e40d |
The accelerating developments in machine learning and reinforcement learning have increased the interest of the control community in using data-driven techniques. Specifically in the area of control of distributed and multi-agent systems, very recent developments include algorithms for multi-agent reinforcement learnin... | i | c14e021f8839a9710caf9e22cc2581f0 |
Autonomy will play an important role in future complex missions where multiple assets act independently{{cite:5346ef8cac3c1e29445524a7f5b97375607be5fe}}. Localization, or more generally state estimation, is one of the key components in establishing autonomy. Due to the absence of global positioning systems, dead-reckon... | i | d72c38e0bcf4fd1bdd42e2597ab34bfc |
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