From openreview.net
Evaluating the Robustness of Analogical Reasoning in Large Language...
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Large language models (LLMs) have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities. However, there is debate on the extent to which they are...
on Mar 17
From openreview.net
On Diffusion Modeling for Anomaly Detection
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Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of...
on Mar 15
From openreview.net
SIMPL: Scalable and hassle-free optimisation of neural...
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Neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related variables. A long-standing goal of neural data analysis has been to identify these variables and...
on Mar 13
From openreview.net
Truncation Is All You Need: Improved Sampling Of Diffusion Models...
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State-of-the-art Denoising Diffusion Probabilistic Models (DDPMs) rely on an expensive sampling process with a large Number of Function Evaluations (NFEs) to provide high-fidelity predictions. This...
on Feb 19
From openreview.net
Explanation Shift: How Did the Distribution Shift Impact the Model?
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The performance of machine learning models on new data is critical for their success in real-world applications. Current methods to detect shifts in the input or output data distributions have...
on Feb 10
From openreview.net
ContextGNN: Beyond Two-Tower Recommendation Systems
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Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of...
on Feb 4
From openreview.net
Compression Represents Intelligence Linearly
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There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the...
on Feb 4
From openreview.net
TopoNets: High performing vision and language models with...
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Neurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs...
on Jan 30
From openreview.net
Learning Distributions of Complex Fluid Simulations with Diffusion...
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Physical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications, it is crucial to access the full...
on Jan 27
From openreview.net
Lessons From Red Teaming 100 Generative AI Products
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In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there is significant debate about how red...
on Jan 17
From openreview.net
VLAS: Vision-Language-Action Model with Speech Instructions for...
0 1
Vision-language-action models (VLAs) have recently become highly prevalent in robot manipulation due to its end-to-end architecture and impressive performance. However, current VLAs are limited to...
on Jan 16
From openreview.net
LLM Merging Competition Technical Report for NeurIPS 2024:...
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We present our solution for the LLM Merging Competition: Building LLMs Efficiently through Merging at NeurIPS 2024. We experimented with a range of base models and merging strategies, ultimately...
on Jan 12
From openreview.net
Model Merging using Geometric Median of Task Vectors
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Training high-performing large language models (LLMs) from scratch is an expensive and complex task. Model merging techniques offer a more computationally efficient alternative, where pretrained...
on Jan 12
From openreview.net
LLM Merging Competition Technical Report: Efficient Model Merging...
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The LLM Merging Competition in NeurIPS’24 aims to build LLMs efficiently through model merging, which enables the combination of multiple specialized fine-tuned models into a single model without...
on Jan 12
From openreview.net
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At the NeurIPS 2024 LLM-Merging competition, we successfully developed a simple and effective model merging approach that generates a versatile, generalist model, applicable to a wide range of...
on Jan 12
From openreview.net
Simple Llama Merge: What Kind of LLM Do We Need?
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Model merging involves integrating multiple specialized models into a single, more powerful model. This approach provides several advantages, including decreased storage and serving costs, enhanced...
on Jan 12
From openreview.net
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Welcome to the OpenReview homepage for NeurIPS 2024 Competition LMC
on Jan 12
From openreview.net
Function Basis Encoding of Numerical Features in Factorization...
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Factorization machine (FM) variants are widely used for large scale real-time content recommendation systems, since they offer an excellent balance between model accuracy and low computational...
on Jan 5
From openreview.net
Putnam-AXIOM: A Functional and Static Benchmark for Measuring...
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As large language models (LLMs) continue to advance, many existing benchmarks designed to evaluate their reasoning capabilities are becoming saturated. Therefore, we present the Putnam-AXIOM...
on Jan 3
From openreview.net
Putnam-AXIOM: A Functional and Static Benchmark for Measuring...
0 0
As large language models (LLMs) continue to advance, many existing benchmarks designed to evaluate their reasoning capabilities are becoming saturated. Therefore, we present the Putnam-AXIOM...
on Jan 1
From openreview.net
Unified Lookup Tables: Privacy-Preserving Foundation Models
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Transformers, despite their success in a variety of sequence modeling tasks, have a significant limitation: they are inherently data-greedy, which can lead to overfitting when the data are scarce....
on Dec 15
From openreview.net
Relating Hopfield Networks to Episodic Control
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Neural Episodic Control is a powerful reinforcement learning framework that employs a differentiable dictionary to store non-parametric memories. It was inspired by episodic memory on the...
on Dec 12
From openreview.net
Visual Autoregressive Modeling: Scalable Image Generation via...
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We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution...
on Dec 12
From openreview.net
ACM TheWebConf 2025 Workshop MORE
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Welcome to the OpenReview homepage for ACM TheWebConf 2025 Workshop MORE
on Dec 11
From openreview.net
MonkeySee: Space-time-resolved reconstructions of natural images...
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In this paper, we reconstruct naturalistic images directly from macaque brain signals using a convolutional neural network (CNN) based decoder. We investigate the ability of this CNN-based decoding...
on Nov 19
From openreview.net
A Path Towards Autonomous Machine Intelligence
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How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple...
on Nov 14
From openreview.net
N$\mathsf{L}^2$PS: A Natural Language to LEAN Proofs System
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The inference capabilities of large language models (LLMs) are rapidly advancing, nearing the limits of current benchmarks. Notably, models like Llama3 have shown substantial improvements on MATH...
on Nov 11
From openreview.net
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Welcome to the OpenReview homepage for NeurIPS 2024 Workshop MATH-AI
on Oct 20
From openreview.net
AGaLiTe: Approximate Gated Linear Transformers for Online...
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In this paper we investigate transformer architectures designed for partially observable online reinforcement learning. The self-attention mechanism in the transformer architecture is capable of...
on Oct 18
From openreview.net
Learned feature representations are biased by complexity, learning...
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Representation learning, and interpreting learned representations, are key areas of focus in machine learning and neuroscience. Both fields generally use representations as a means to understand or...
on Sep 24
From openreview.net
Language Modeling Is Compression
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It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on...
on Sep 4
From openreview.net
Projected Language Models: A Large Model Pre-Segmented Into Smaller...
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Large language models are versatile tools but are not suitable for small inference budgets. Small models have more efficient inference but their lower capacity means that their performance can be...
on Jul 23
From openreview.net
Gradual Optimization Learning for Conformational Energy Minimization
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Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization methods that use...
on Jul 23
From openreview.net
Don’t Label Twice: Quantity Beats Quality when Comparing Binary...
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We study how to best spend a budget of noisy labels to compare the accuracy of two binary classifiers. It’s common practice to collect and aggregate multiple noisy labels for a given data point...
on Jul 18
From openreview.net
Position: Enforced Amnesia as a Way to Mitigate the Potential Risk...
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Science fiction has explored the possibility of a conscious self-aware mind being locked in silent suffering for prolonged periods of time. Unfortunately, we still do not have a reliable test for...
on Jul 15
From openreview.net
SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code
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This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets....
on Jun 26
From openreview.net
VerityMath: Advancing Mathematical Reasoning by Self-Verification...
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Large Language Models (LLMs), combined with program-based solving techniques, are increasingly demonstrating proficiency in mathematical reasoning. For example, closed-source models such as OpenAI...
on Jun 26
From openreview.net
PutnamBench: A Multilingual Competition-Mathematics Benchmark for...
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We present PutnamBench, a new benchmark for evaluating the ability of neural theorem-provers to solve competition mathematics problems. PutnamBench consists of formalizations of problems sourced...
on Jun 26
From openreview.net
Lean4trace: Data augmentation for neural theorem proving in Lean
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Integrating large language models as proof assistants with theorem provers has shown great promise. However, one of the major challenges in this field is the scarcity of training data. To address...
on Jun 26
From openreview.net
More Details, Please: Improving Autoformalization with More...
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The formalization of mathematical theorems and their proofs is a time-consuming and tedious process which, despite recent advances in the reasoning capabilities of AI systems, remains a challenging...
on Jun 26
From openreview.net
Logic-LM: Empowering Large Language Models with Symbolic Solvers...
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Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs...
on May 28
From openreview.net
SpaRC and SpaRP: Spatial Reasoning Characterization and Path...
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Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language...
on May 27
From openreview.net
Vision Transformers Need Registers
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Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised and...
on May 11
From openreview.net
Modeling Boundedly Rational Agents with Latent Inference Budgets
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We study the problem of modeling a population of agents pursuing unknown goals subject to unknown computational constraints. In standard models of bounded rationality, sub-optimal decision-making...
on May 9
From openreview.net
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Welcome to the OpenReview homepage for ICLR 2024 Workshop AfricaNLP
on May 2
From openreview.net
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Welcome to the OpenReview homepage for Wiki Workshop 2024 Hall
on Mar 18, 2024