Karol Arndt

24 papers A* 3A 2Journal 17Unranked 1
YearRankTypeTitle / Venue / Authors
2025 A* conf
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport.
CVPR
Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Oliver Struckmeier, Karol Arndt, Markus Heinonen, Ville Kyrki, Samuel Kaski
2023 J jnl
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation.
CoRR
Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
2023 A* conf
Co-imitation: Learning Design and Behaviour by Imitation.
AAAI
Chang Rajani, Karol Arndt, David Blanco Mulero, Kevin Sebastian Luck, Ville Kyrki
2023 J jnl
DROPO: Sim-to-real transfer with offline domain randomization.
Robotics Auton. Syst.
Gabriele Tiboni, Karol Arndt, Ville Kyrki
2023 J jnl
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation.
Trans. Mach. Learn. Res.
Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
2023 — —
Safe and efficient transfer of robot policies from simulation to the real world.
Karol Arndt
2023 J jnl
Understanding deep neural networks through the lens of their non-linearity.
CoRR
Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski
2022 J jnl
Co-Imitation: Learning Design and Behaviour by Imitation.
CoRR
Chang Rajani, Karol Arndt, David Blanco Mulero, Kevin Sebastian Luck, Ville Kyrki
2022 J jnl
DROPO: Sim-to-Real Transfer with Offline Domain Randomization.
CoRR
Gabriele Tiboni, Karol Arndt, Ville Kyrki
2022 — conf
Online vs. Offline Adaptive Domain Randomization Benchmark.
HFR
Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi
2022 J jnl
Online vs. Offline Adaptive Domain Randomization Benchmark.
CoRR
Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi
2022 J jnl
SafeAPT: Safe Simulation-to-Real Robot Learning Using Diverse Policies Learned in Simulation.
IEEE Robotics Autom. Lett.
Rituraj Kaushik, Karol Arndt, Ville Kyrki
2022 J jnl
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation.
CoRR
Rituraj Kaushik, Karol Arndt, Ville Kyrki
2022 J jnl
Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models.
J. Mach. Learn. Res.
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman
2022 J jnl
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models.
CoRR
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman
2021 J jnl
Affine Transport for Sim-to-Real Domain Adaptation.
CoRR
Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki
2021 A conf
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation.
IROS
Karol Arndt, Oliver Struckmeier, Ville Kyrki
2021 J jnl
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation.
CoRR
Karol Arndt, Oliver Struckmeier, Ville Kyrki
2021 J jnl
Few-Shot Model-Based Adaptation in Noisy Conditions.
IEEE Robotics Autom. Lett.
Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki
2020 J jnl
Few-shot model-based adaptation in noisy conditions.
CoRR
Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki
2020 A* conf
Meta Reinforcement Learning for Sim-to-real Domain Adaptation.
ICRA
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki
2019 A conf
Affordance Learning for End-to-End Visuomotor Robot Control.
IROS
Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki
2019 J jnl
Affordance Learning for End-to-End Visuomotor Robot Control.
CoRR
Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki
2019 J jnl
Meta Reinforcement Learning for Sim-to-real Domain Adaptation.
CoRR
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki
redb/extractors/enum.py
← Index redb/extractors/enum.py python 
from enum import Enum


class Tag(Enum):
    BASIC_PROPERTIES = "basic_properties"
    DIEC = "detect_it_easy"
    CAPA = "capa"
    MALCONTENT = "malcontent"
    DECOMPILED = "decompiled"
    HASH = "hash"
    STRINGS = "strings"
    RELATED = "relatedness" # unused
    PE_FEATURES = "pe_features"
    PE_IMPORT = "pe_import"
    PE_OVERLAY = "pe_overlay"
    PE_SECTION = "pe_section"
    PE_RESOURCE = "pe_resource"
    PE_SIGNATURE = "pe_signature"
    PE_CERTIFICATE = "pe_certificate"
    PE_SIGNER = "pe_signer"
    PE_DOTNET = "pe_dotnet"
    PE_EMBEDDED_EXTRAS = "pe_embedded_extras"
    PE_INCONSISTENCY_TESTS = "pe_inconsistency_tests"
    DOTNET_INCONSISTENCY_TESTS = "dotnet_inconsistency_tests"
    YARA = "yara"
    # Mach-O
    MACHO_FEATURES = "macho_features"
    MACHO_IMPORT = "macho_import"
    MACHO_EXPORT = "macho_export"
    MACHO_DYLIB = "macho_dylib"
    MACHO_SEGMENT = "macho_segment"
    MACHO_SIGNATURE = "macho_signature"
    MACHO_CERTIFICATE = "macho_certificate"
    MACHO_ENTITLEMENT = "macho_entitlement"
    MACHO_UNIVERSAL = "macho_universal"
    MACHO_HASHES = "macho_hashes"
    # ELF
    ELF_FEATURES = "elf_features"
    ELF_IMPORT = "elf_import"
    ELF_EXPORT = "elf_export"
    ELF_SECTION = "elf_section"
    ELF_SEGMENT = "elf_segment"
    ELF_SYMBOL = "elf_symbol"
    ELF_RELOCATION = "elf_relocation"
    ELF_NOTE = "elf_note"
    ELF_DEPENDENCY = "elf_dependency"
    # APK
    APK_FEATURES = "apk_features"
    APK_MANIFEST = "apk_manifest"
    APK_PERMISSIONS = "apk_permissions"
    APK_SIGNATURE = "apk_signature"
    APK_DEX = "apk_dex"
    APK_RESOURCES = "apk_resources"
    APK_NATIVE_LIBS = "apk_native_libs"
    APK_INCONSISTENCY_TESTS = "apk_inconsistency_tests"
    APK_DECOMPILED = "apk_decompiled"
    # JavaScript
    JS_FEATURES = "js_features"
    JS_SUSPICIOUS_APIS = "js_suspicious_apis"
    JS_STRINGS = "js_strings"
    JS_DEOBFUSCATION = "js_deobfuscation"
    JS_CONTENT = "js_content"