5 citations · 20 across the 30 of their papers we have counts for
9 papers · 1 filter
Rethinking the shape convention of an MLP
Meng-Hsi Chen, Yu-Ang Lee, Feng-Ting Liao +1
Multi-layer perceptrons (MLPs) conventionally follow a narrow-wide-narrow design where skip connections operate at the input/output dimensions while processing occurs in expanded h…
Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds
Aya Kayal, Sattar Vakili, Laura Toni +2
Bayesian optimization (BO) with preference-based feedback has recently garnered significant attention due to its emerging applications. We refer to this problem as Bayesian Optimiz…
Latent Flow Transformer
Yen-Chen Wu, Feng-Ting Liao, Meng-Hsi Chen +3
Transformers, the standard implementation for large language models (LLMs), typically consist of tens to hundreds of discrete layers. While more layers can lead to better performan…
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
Davide Buffelli, Jamie McGowan, Wangkun Xu +4
Second-order optimization has been shown to accelerate the training of deep neural networks in many applications, often yielding faster progress per iteration on the training loss…
Flexible Multiple-Objective Reinforcement Learning for Chip Placement
Fu-Chieh Chang, Yu-Wei Tseng, Ya-Wen Yu +11
Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the…
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Sattar Vakili, Jonathan Scarlett, Da-shan Shiu +1
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization. It is well known that a…
Uniform Generalization Bounds for Overparameterized Neural Networks
Sattar Vakili, Michael Bromberg, Jezabel Garcia +2
An interesting observation in artificial neural networks is their favorable generalization error despite typically being extremely overparameterized. It is well known that the clas…
Meta-Learning with MAML on Trees
Jezabel R. Garcia, Federica Freddi, Feng-Ting Liao +5
In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tas…
How to distribute data across tasks for meta-learning?
Alexandru Cioba, Michael Bromberg, Qian Wang +5
Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones. They are trained on benchmarks with a fixed number of data points per task. This…