Zhixuan Lin
Scholar

Zhixuan Lin

Google Scholar ID: BiyrJeMAAAAJ
University of Montreal; Mila
Sequence ModelingReinforcement Learning
Citations & Impact
All-time
Citations
512
 
H-index
4
 
i10-index
4
 
Publications
6
 
Co-authors
0
 
Resume (English only)
Academic Achievements
  • Adaptive Computation Pruning for the Forgetting Transformer, COLM 2025 (First author).
  • Forgetting Transformer: Softmax Attention with a Forget Gate, ICLR 2025 (First author).
  • The Curse of Diversity in Ensemble-Based Exploration, ICLR 2024 (First author).
  • Improving Generative Imagination in Object-Centric World Models, ICML 2020 (First author).
  • SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition, ICLR 2020 (Co-first author, marked with *).
  • GIFT: Learning Transformation Invariant Dense Visual Descriptors via Group CNNs, NeurIPS 2019 (Collaborator).
Background
  • Third-year Ph.D. student at Mila and the University of Montreal, advised by Professor Aaron Courville.
  • Research goal is to understand and build general intelligence; currently focused on long-context sequence models (especially linear-complexity models) and their applications in reinforcement learning (RL).
  • Views an agent fundamentally as a sequence model, emphasizing the temporal and sequential nature of agent-environment interaction (memory, experience stream, learning, credit assignment, etc.) as central to intelligence.
  • Believes RL is likely necessary for superhuman intelligence, but simple future prediction—not just reward-based learning—may remain essential due to limited learning signals.
  • Intrigued by the inefficiency of human thought and reasoning in natural language, and questions whether neural networks can develop mathematical or high-dimensional geometric intuition beyond human capabilities.
Co-authors
0 total
Co-authors: 0 (list not available)