Konečný - Výuka - KMI/UMIN Umělá inteligence
Rozvrh předmětu
Přednáška: pondělí: 13:15 - 14:45
Cvičení: pondělí: 15:00 - 15:45
Výukové materiály
Studijní materiály
Cvičení
https://github.com/konyconi/UMIN_AI_exercises/
Témata referátů
1) [Smajzr Michal]
LoRA: Low-Rank Adaptation of Large Language Models
https://arxiv.org/abs/2106.09685
2)
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
https://arxiv.org/abs/2101.03961
3) [Litschmann Jakub]
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
https://arxiv.org/abs/1711.11279
4) [Natalie Trhlíkova]
Adversarial Examples Are Not Bugs, They Are Features
https://arxiv.org/abs/1905.02175
5) [Juránková Anita]
Language Models are Multilingual Chain-of-Thought Reasoners
https://arxiv.org/abs/2210.03057
6) [Votočka David]
The Ethics of Artificial Intelligence
https://nickbostrom.com/ethics/artificial-intelligence.pdf
7)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://arxiv.org/abs/1810.04805
8) [Loučka Richard Bohuslav]
Logical Neural Networks
https://arxiv.org/abs/2006.13155
9) [Škrabalová Eliška]
Neural Logic Networks
https://arxiv.org/abs/1910.08629
10) [Hrdina Filip]
Learning Algorithms via Neural Logic Networks
https://arxiv.org/abs/1904.01554
11)
A Survey of the State of Explainable AI for Natural Language Processing
https://arxiv.org/abs/2010.00711
12) [Tomáš Kudělka]
Differentiable Logics for Neural Network Training and Verification
https://arxiv.org/abs/2207.06741
13) [Jan Lakomý]
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
https://arxiv.org/abs/2010.15581
14)
Abduction and Argumentation for Explainable Machine Learning: A Position Survey
https://arxiv.org/abs/2010.12896
15) [Jiří Kvapil]
Neural Logic Analogy Learning
https://arxiv.org/abs/2202.02436
16) [Alžbeta Rástocká]
Neural Symbolic Logical Rule Learner for Interpretable Learning
https://arxiv.org/abs/2408.11918
17)
Logic Gate Neural Networks are Good for Verification
https://arxiv.org/abs/2505.19932
18)
Categorical Construction of Logically Verifiable Neural Architectures
https://arxiv.org/abs/2508.11647
19) [Pastorek Mojmír]
Standard Neural Computation Alone Is Insufficient for Logical Intelligence
https://arxiv.org/abs/2502.02135
20) [Martin Podmanický]
AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research
https://arxiv.org/abs/2505.12039
21) [Vincour Radek]
AI Agents: Evolution, Architecture, and Real-World Applications
https://arxiv.org/abs/2503.12687
22)
Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services
https://arxiv.org/abs/2010.04827
23) [Romančíková Paulína]
Generative Pretraining from Pixels
https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf
24)
Language Models are Few-Shot Learners
https://arxiv.org/abs/2005.14165
25) [Kárný Tomáš]
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://arxiv.org/abs/2010.11929
26) [Michael Široký]
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://arxiv.org/abs/2103.14030
27) [Davies Tomáš]
Zero-Shot Text-to-Image Generation
https://arxiv.org/abs/2102.12092
28)
Learning Transferable Visual Models From Natural Language Supervision
https://arxiv.org/abs/2103.00020
29) [Thomas Berger]
High-Resolution Image Synthesis with Latent Diffusion Models
https://arxiv.org/abs/2112.10752
30)
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
https://arxiv.org/abs/2211.05100
31) [Kalenda Martin]
Reinforcement Learning from Human Feedback
https://arxiv.org/abs/2504.12501
32)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://arxiv.org/abs/1703.03400
33) [Kašparová Sofie]
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
https://arxiv.org/abs/1803.03635
34)
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
https://arxiv.org/abs/1806.07572
35) [Kercl Aleš]
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
https://arxiv.org/abs/1911.08265
36) [Patrik Kubatka]
Deep Double Descent: Where Bigger Models and More Data Hurt
https://arxiv.org/abs/1912.02292
37) [Smékal Samuel]
Graph Neural Networks: A Review of Methods and Applications
https://arxiv.org/abs/1812.08434
38) [Malíček Filip]
Prototypical Networks for Few-shot Learning
https://arxiv.org/abs/1703.05175
39) [Čapka Tomáš]
Reinforcement Learning with Unsupervised Auxiliary Tasks
https://arxiv.org/abs/1611.05397
40)
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://arxiv.org/abs/2205.14135
41) [Vojtěch Netrh]
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://arxiv.org/abs/2005.11401
Výukové materiály (z 2024)