Kati8/ICLR_2023_2025 huggingface dataset:
authors: Shweta Singh
authors: Vedant Ghatnekar
authors: Sudaman Rajesh Katti
reviews: 
Comments: The paper discusses the use of memory mechanisms in language processing and reinforcement learning agents. It introduces the concept of Automatic Chunking, a model that combines the TransformerXL architecture with a cyclic memory buffer and chunk selection mechanism. The model is tested on two tasks: an audio-visual instructions task and a visual corridor task with a variable distractor. The results show that Automatic Chunking with ForgetSpan improves training performance and memory efficiency. The model demonstrates the ability to learn a proper mapping between audio and visual observations and recall information after a variable distractor phase.
Strengths: This paper offers a pioneering approach to enhancing memory efficiency in transformers by emulating human cognitive processes. Its introduction of a modified TransformerXL architecture is both innovative and timely. The paper further stands out with its trio of novel techniques: Automatic Chunking, ForgetSpan, and Similarity based forgetting, each addressing unique memory optimization challenges. Comprehensive testing across diverse tasks not only validates the model's robustness but also underscores its relevance in real-world robotic applications. In essence, the paper shines in its blend of innovation, rigorous evaluation, and practical applicability.
Weaknesses: - Some statements require more evidence and clarification.
  - Introduction: "Human cognition and decision-making works on reflection on only relevant parts of memory.". Should add citations of related works to support argument.
  - Introduction: "Robotic agents should have similar cognition to function well in long horizon and multi-modal tasks like navigation or human-robot collaboration.". Should add citations of related works or analysis to support argument.
- Lack of comparative analysis
  - The paper primarily compares the proposed model with the baseline TransformerXL. A more extensive comparison with other state-of-the-art models might provide a clearer picture of the model's standing in the current research landscape.
- Limited discussion about result
  - Most discussions only state the data provided in the figure and concluded as "our method is better than baseline", and further discussion about the result is missing.
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 1 poor
reviews: 
Comments: This paper attempts to introduce a method for long-horizon episodic decision-making for cognitively inspired robots.
Strengths: + The problem of long-horizon episodic decision-making seems important for cognitively inspired robots, although not well justified in the paper.
Weaknesses: - The paper's novelty is considerably low.

- The paper does not show an understanding of the state of the art.

- Experimental results cannot justify the novelty or contribution of the proposed approach.

- The paper is poorly written with many grammar errors and typos.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 1 poor
Clarity: 1 poor
reviews: 
Comments: This paper proposes a novel transformer architecture TransformerXL with improved memory efficiency, achieved through selective encoding of memories that contribute to learning, and discarding memories determined to be not relevant. This project seems to be in an early stage, there is no related work section, and methodology section does not explain the model well enough for someone to try to replicate it.
Strengths: I think the memory implementation is original, but because of poor presentation it is hard to understand the significance of the contribution.
Weaknesses: This paper is not ready yet for a conference presentation, it is an early stage technical report.
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 1 poor