This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.
This repository contains two sets of experiments, one for the UCI electricity dataset, and one on historical S&P500 stock prices.
In both the Electricity and Finance folders, you will find another README file describing how to run the experiments.
Gagandeep Singh
Pavol Bielik
Martin Vechev
https://github.com/eth-sri/probabilistic-forecasts-attacks/blob/df78ffd7375089bb7b38fbf55bb840b7699365eb/Finance/attack/attacks.py#L91-L116
Hi, one more thing.
I suppose the first loss.backward() here calculates ''mean.grad'' , aux_estimate.backward() calculates "aux_estimate.grad", and the second loss.backward() computes 'perturbation.grad'. Could you provide the relationship between these three gradient?
My guess is : perturbation.grad = mean.grad * aux_estimate.grad, is this correct?
i.e.:
https://github.com/eth-sri/probabilistic-forecasts-attacks/blob/df78ffd7375089bb7b38fbf55bb840b7699365eb/Finance/attack/attacks.py#L104-L109
Hi, what does it mean here to 'Multiply the two, and set it in perturbation', could you provide a mathematical description? I think it has something to do with two gradients.
https://github.com/eth-sri/probabilistic-forecasts-attacks/blob/df78ffd7375089bb7b38fbf55bb840b7699365eb/Finance/attack/attack_modules.py#L299-L317
Hi, could you provide some explanation for the 'aux_estimate, mean, sum_first_moment' here?
It would be great if you can give both some description and math formula/definition of these three variables here.
I appreciate your work!
Bumps urllib3 from 1.25.6 to 1.25.8.
Sourced from urllib3's releases.
1.25.8
Release: 1.25.8
1.25.7
No release notes provided.
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1.25.8 (2020-01-20)
1.25.7 (2019-11-11)
Preserve
chunkedparameter on retries (Pull #1715, Pull #1734)Allow unset
SERVER_SOFTWAREin App Engine (Pull #1704, Issue #1470)Fix issue where URL fragment was sent within the request target. (Pull #1732)
Fix issue where an empty query section in a URL would fail to parse. (Pull #1732)
Remove TLS 1.3 support in SecureTransport due to Apple removing support (Pull #1703)
2a57bc5 Release 1.25.8 (#1788)a2697e7 Optimize _encode_invalid_chars (#1787)d2a5a59 Move IPv6 test skips in server fixturesd44f0e5 Factorize test certificates serialization84abc7f Generate IPV6 certificates using trustme6a15b18 Run IPv6 Tornado server from fixture4903840 Use trustme to generate IP_SAN cert9971e27 Empty responses should have no lines.62ef68e Use trustme to generate NO_SAN certsfd2666e Use fixture to configure NO_SAN test certsDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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Bumps certifi from 2019.9.11 to 2022.12.7.
9e9e840 2022.12.07b81bdb2 2022.09.24939a28f 2022.09.14aca828a 2022.06.15.2de0eae1 Only use importlib.resources's new files() / Traversable API on Python ≥3.11 ...b8eb5e9 2022.06.15.147fb7ab Fix deprecation warning on Python 3.11 (#199)b0b48e0 fixes #198 -- update link in license9d514b4 2022.06.154151e88 Add py.typed to MANIFEST.in to package in sdist (#196)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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Bumps joblib from 0.14.0 to 1.2.0.
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Release 1.2.0
Fix a security issue where
eval(pre_dispatch)could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256
Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263
Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with
mmap_mode != Noneas the resultingnumpy.memmapobject would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.
Vendor loky 3.3.0 which fixes several bugs including:
robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);
avoiding leaking worker processes in case of nested loky parallel calls;
reliability spawn the correct number of reusable workers.
Release 1.1.0
Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. joblib/joblib#1181
Fix joblib.Memory bug with the
ignoreparameter when the cached function is a decorated function.
... (truncated)
5991350 Release 1.2.03fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)cea26ff CI test the future loky-3.3.0 branch (#1338)8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)ac4ebd5 MAINT add back pytest warnings plugin (#1337)a23427d Test child raises parent exits cleanly more reliable on macos (#1335)ac09691 [MAINT] various test updates (#1334)4a314b1 Vendor loky 3.2.0 (#1333)bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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Bumps numpy from 1.14.3 to 1.22.0.
Sourced from numpy's releases.
v1.22.0
NumPy 1.22.0 Release Notes
NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:
- Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
- A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
- NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
- New methods for
quantile,percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.- A new configurable allocator for use by downstream projects.
These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.
The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.
Expired deprecations
Deprecated numeric style dtype strings have been removed
Using the strings
"Bytes0","Datetime64","Str0","Uint32", and"Uint64"as a dtype will now raise aTypeError.(gh-19539)
Expired deprecations for
loads,ndfromtxt, andmafromtxtin npyio
numpy.loadswas deprecated in v1.15, with the recommendation that users usepickle.loadsinstead.ndfromtxtandmafromtxtwere both deprecated in v1.17 - users should usenumpy.genfromtxtinstead with the appropriate value for theusemaskparameter.(gh-19615)
... (truncated)
4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-releasefd66547 REL: Prepare for the NumPy 1.22.0 release.125304b wipc283859 Merge pull request #20682 from charris/backport-204165399c03 Merge pull request #20681 from charris/backport-20954f9c45f8 Merge pull request #20680 from charris/backport-20663794b36f Update armccompiler.pyd93b14e Update test_public_api.py7662c07 Update init.py311ab52 Update armccompiler.pyDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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Bumps numpy from 1.17.2 to 1.22.0.
Sourced from numpy's releases.
v1.22.0
NumPy 1.22.0 Release Notes
NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:
- Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
- A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
- NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
- New methods for
quantile,percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.- A new configurable allocator for use by downstream projects.
These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.
The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.
Expired deprecations
Deprecated numeric style dtype strings have been removed
Using the strings
"Bytes0","Datetime64","Str0","Uint32", and"Uint64"as a dtype will now raise aTypeError.(gh-19539)
Expired deprecations for
loads,ndfromtxt, andmafromtxtin npyio
numpy.loadswas deprecated in v1.15, with the recommendation that users usepickle.loadsinstead.ndfromtxtandmafromtxtwere both deprecated in v1.17 - users should usenumpy.genfromtxtinstead with the appropriate value for theusemaskparameter.(gh-19615)
... (truncated)
4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-releasefd66547 REL: Prepare for the NumPy 1.22.0 release.125304b wipc283859 Merge pull request #20682 from charris/backport-204165399c03 Merge pull request #20681 from charris/backport-20954f9c45f8 Merge pull request #20680 from charris/backport-20663794b36f Update armccompiler.pyd93b14e Update test_public_api.py7662c07 Update init.py311ab52 Update armccompiler.pyDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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Hello, I am getting the following error while running python dataset/generation/dataset.py in the Financial directory:
Traceback (most recent call last):
File "dataset/generation/dataset.py", line 262, in <module>
generate_dataset(args)
File "dataset/generation/dataset.py", line 250, in generate_dataset
Dataset(year,args.input_length+1,args.output_length,args.batch_size)
File "dataset/generation/dataset.py", line 52, in __init__
start_test_year,end_test_year = self.read_data(year)
File "dataset/generation/dataset.py", line 74, in read_data
year_indexes = np.where(self.data.index.year==year)
AttributeError: 'Index' object has no attribute 'year'
Any suggestion?
Bumps urllib3 from 1.25.6 to 1.26.5.
Sourced from urllib3's releases.
1.26.5
:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap
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1.26.4
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- Changed behavior of the default
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1.26.3
:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap
Fixed bytes and string comparison issue with headers (Pull #2141)
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- Fixed an issue where
wrap_socketandCERT_REQUIREDwouldn't be imported properly on Python 2.7.8 and earlier (Pull #2052)1.26.1
:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap
- Fixed an issue where two
User-Agentheaders would be sent if aUser-Agentheader key is passed asbytes(Pull #2047)1.26.0
:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap
Added support for HTTPS proxies contacting HTTPS servers (Pull #1923, Pull #1806)
Deprecated negotiating TLSv1 and TLSv1.1 by default. Users that still wish to use TLS earlier than 1.2 without a deprecation warning should opt-in explicitly by setting
ssl_version=ssl.PROTOCOL_TLSv1_1(Pull #2002) Starting in urllib3 v2.0: Connections that receive aDeprecationWarningwill failDeprecated
RetryoptionsRetry.DEFAULT_METHOD_WHITELIST,Retry.DEFAULT_REDIRECT_HEADERS_BLACKLISTandRetry(method_whitelist=...)in favor ofRetry.DEFAULT_ALLOWED_METHODS,Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT, andRetry(allowed_methods=...)(Pull #2000) Starting in urllib3 v2.0: Deprecated options will be removed
... (truncated)
Sourced from urllib3's changelog.
1.26.5 (2021-05-26)
- Fixed deprecation warnings emitted in Python 3.10.
- Updated vendored
sixlibrary to 1.16.0.- Improved performance of URL parser when splitting the authority component.
1.26.4 (2021-03-15)
- Changed behavior of the default
SSLContextwhen connecting to HTTPS proxy during HTTPS requests. The defaultSSLContextnow setscheck_hostname=True.1.26.3 (2021-01-26)
Fixed bytes and string comparison issue with headers (Pull #2141)
Changed
ProxySchemeUnknownerror message to be more actionable if the user supplies a proxy URL without a scheme. (Pull #2107)1.26.2 (2020-11-12)
- Fixed an issue where
wrap_socketandCERT_REQUIREDwouldn't be imported properly on Python 2.7.8 and earlier (Pull #2052)1.26.1 (2020-11-11)
- Fixed an issue where two
User-Agentheaders would be sent if aUser-Agentheader key is passed asbytes(Pull #2047)1.26.0 (2020-11-10)
NOTE: urllib3 v2.0 will drop support for Python 2.
Read more in the v2.0 Roadmap <https://urllib3.readthedocs.io/en/latest/v2-roadmap.html>_.Added support for HTTPS proxies contacting HTTPS servers (Pull #1923, Pull #1806)
Deprecated negotiating TLSv1 and TLSv1.1 by default. Users that still wish to use TLS earlier than 1.2 without a deprecation warning
... (truncated)
d161647 Release 1.26.52d4a3fe Improve performance of sub-authority splitting in URL2698537 Update vendored six to 1.16.007bed79 Fix deprecation warnings for Python 3.10 ssl moduled725a9b Add Python 3.10 to GitHub Actions339ad34 Use pytest==6.2.4 on Python 3.10+f271c9c Apply latest Black formatting1884878 [1.26] Properly proxy EOF on the SSLTransport test suitea891304 Release 1.26.48d65ea1 Merge pull request from GHSA-5phf-pp7p-vc2rDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2：ImageNet无限制对抗攻击 介绍 ： 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。
Non-AR Spatial-Temporal Transformer Introduction Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series For
Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet, CVPR2021 安全AI挑战者计划第六期：ImageNet无限制对抗攻击 决赛第四名（team name: Advers）
Spacetimeformer Multivariate Forecasting This repository contains the code for the paper, "Long-Range Transformers for Dynamic Spatiotemporal Forecast
event-forecasting Event Forecasting Algorithms Theory Correlating events in comp
What is judgyprophet? judgyprophet is a Bayesian forecasting algorithm based on Prophet, that enables forecasting while using information known by the
Stronger and Faster Wasserstein Adversarial Attacks Code for Stronger and Faster Wasserstein Adversarial Attacks, appeared in ICML 2020. This reposito
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ([email protected]), Marinka Zitnik (marinka@hms.
EMI-FGSM This repository contains code to reproduce results from the paper: Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021) Xiaosen Wa
Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Stable Neural ODE with Lyapunov-Stable Equilibrium
Minimal code and simple experiments to play with Denoising Diffusion Probabilist
Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Anytime Autoregressive Model Anytime Sampling for Autoregressive Models via Ordered Autoencoding , ICLR 21 Yilun Xu, Yang Song, Sahaj Gara, Linyuan Go
GANVAS-models This is an implementation of various generative models. It contains implementations of the following: Autoregressive Models: PixelCNN, G
RITA: a Study on Scaling Up Generative Protein Sequence Models RITA is a family of autoregressive protein models, developed by a collaboration of Ligh
Official PyTorch implementation for "On Fast Sampling of Diffusion Probabilistic Models". FastDPM generation on CIFAR-10, CelebA, and LSUN datasets. S
Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois
ILVR + ADM This is the implementation of ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral). This repository is h
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